# Best Big Data Analytics Software - Page 2 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Big data analytics software provides insights into large, complex data sets collected from big data clusters, helping business users understand data trends, patterns, and anomalies through visualizations, reports, and dashboards, often requiring query languages to extract data from unstructured file systems. ### Core Capabilities of Big Data Analytics Software To qualify for inclusion in the Big Data Analytics category, a product must: - Consume data, query file systems, and connect directly to big data clusters - Allow users to prepare complex big data sets into helpful and understandable data visualizations - Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets ### Common Use Cases for Big Data Analytics Software Data engineers, analysts, and business intelligence teams use big data analytics software to extract value from large-scale, unstructured data environments. Common use cases include: - Querying and analyzing large Hadoop or distributed data clusters to surface business insights - Detecting patterns and anomalies in high-volume data sets for operational or strategic decision-making - Building self-service charts and dashboards for non-technical stakeholders from big data sources ### How Big Data Analytics Software Differs from Other Tools Big data analytics software is solely focused on manipulating complex, large-scale data clusters into understandable visualizations, differentiating it from [analytics platforms](https://www.g2.com/categories/analytics-platforms), which support a wide range of data sources and connectors beyond big data. The two categories are mutually exclusive. Big data analytics tools are commonly used at companies running Hadoop in conjunction with [big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) and integrate with [data warehouse software](https://www.g2.com/categories/data-warehouse) as the central hub for integrated data. Some solutions also leverage [machine learning](https://www.g2.com/categories/machine-learning) and [natural language processing](https://www.g2.com/categories/natural-language-processing-nlp) to enable natural language querying. ### Insights from G2 on Big Data Analytics Software Based on category trends on G2, query flexibility and scalability for large data sets stand out as standout capabilities. Faster insight generation from complex data environments stand out as the primary benefit of adoption. ## Top Big Data Analytics Software at a Glance | # | Product | Rating | Best For | What Users Say | |---|---------|--------|----------|----------------| | 1 | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5.0 (1,285 reviews) | Unified lakehouse ETL, analytics, and ML pipelines | "[Great Spark Scaling, But Slow Cluster Boot Times](https://www.g2.com/survey_responses/databricks-review-12905667)" | | 2 | [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) | 4.5/5.0 (1,145 reviews) | Serverless SQL analytics on petabyte-scale datasets | "[Easy-to-Use Cloud Tool with Shareable, Saved Queries](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)" | | 3 | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.5/5.0 (708 reviews) | Elastic multi-workload analytics with zero-infrastructure overhead | "[Easy, Efficient Data Extraction with Clear Database Insights](https://www.g2.com/survey_responses/snowflake-review-12884116)" | | 4 | [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) | 4.4/5.0 (159 reviews) | Federated lakehouse querying across hybrid data environments | "[Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)" | | 5 | [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) | 4.5/5.0 (211 reviews) | Unified Spark-native lakehouse ETL and ML | "[A powerhouse for scaling ML workflows, but keep a close eye on your billing.](https://www.g2.com/survey_responses/azure-databricks-review-12976834)" | | 6 | [Alteryx](https://www.g2.com/products/alteryx/reviews) | 4.6/5.0 (830 reviews) | No-code ETL and multi-source data blending | "[Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights](https://www.g2.com/survey_responses/alteryx-review-12983224)" | | 7 | [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) | 4.8/5.0 (265 reviews) | Sub-second OLAP querying on cloud-scale datasets | "[Kyvos Unified Our Business Logic with a Single Semantic Model](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12797024)" | | 8 | [Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews) | 4.4/5.0 (37 reviews) | Unified ETL and big data warehousing on Azure | "[Unified Data Warehousing and Big Data in One Powerful Platform](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12435130)" | | 9 | [Dataiku](https://www.g2.com/products/dataiku/reviews) | 4.4/5.0 (208 reviews) | End-to-end ML pipelines with low-code collaboration | "[Dataiku: No-Code ETL Powerhouse — Collaborative, Visual, and Python/SQL Friendly](https://www.g2.com/survey_responses/dataiku-review-13046146)" | | 10 | [Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews) | 4.3/5.0 (414 reviews) | Cross-source log correlation and security analytics | "[Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure](https://www.g2.com/survey_responses/splunk-enterprise-review-12045230)" | ## How Many Big Data Analytics Software Products Does G2 Track? **Total Products under this Category:** 110 ### Category Stats (Jul 2026) - **Average Rating**: 4.45/5 (↓0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings - **Top Trending Product**: Savant Labs (+0.93%) - Among all products in this category, Savant Labs recorded the largest rating increase compared to last month *Last updated: July 02, 2026* ## How Does G2 Rank Big Data Analytics Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 8,300+ Authentic Reviews - 110+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Big Data Analytics Software Is Best for Your Use Case? - **Leader:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Highest Performer:** [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) - **Easiest to Use:** [Snowflake](https://www.g2.com/products/snowflake/reviews) - **Top Trending:** [dbt](https://www.g2.com/products/dbt/reviews) - **Best Free Software:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) --- **Sponsored** ### ILUM Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1041&secure%5Bchosen_at%5D=2026-07-02T20%3A50%3A12Z&secure%5Bdisplayable_resource_id%5D=1041&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1041&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1416491&secure%5Bresource_id%5D=1041&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-analytics%3Fopen_modal_url%3D%252Fproducts%252Ftimbr%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fbig-data-analytics%2526source%253Dcategory&secure%5Btoken%5D=4f28f980d5313b57103e455e3044a01589d5bb8408c49c40c23d1830e2f6f5db&secure%5Burl%5D=https%3A%2F%2Filum.cloud%2F%3Futm%3Dg2&secure%5Burl_type%5D=custom_url) --- ## Big Data Analytics Software Features & Capabilities ### What are the Best Big Data Analytics Software with Embedded Analytics? Allows big data tool to run and record data within external applications. **Top-rated Big Data Analytics Software for Embedded Analytics:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Embedded Analytics](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics) ### What are the Best Big Data Analytics Software with Spark Integration? Aligns processing and distribution workflows on top of Apache Spark **Top-rated Big Data Analytics Software for Spark Integration:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Spark Integration](https://www.g2.com/categories/big-data-analytics/f/spark-integration) ### What are the Best Big Data Analytics Software with Governed Discovery? Isolates certain datasets and facilitates management of data access. **Top-rated Big Data Analytics Software for Governed Discovery:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Governed Discovery](https://www.g2.com/categories/big-data-analytics/f/governed-discovery) ### What are the Best Big Data Analytics Software with Notebooks? Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations **Top-rated Big Data Analytics Software for Notebooks:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Alteryx](https://www.g2.com/products/alteryx/reviews) [Explore Big Data Analytics Software with Notebooks](https://www.g2.com/categories/big-data-analytics/f/notebooks) ### What are the Best Big Data Analytics Software with Data Lake? Facilitates the dissemination of collected big data throughout parallel computing clusters. **Top-rated Big Data Analytics Software for Data Lake:** - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) [Explore Big Data Analytics Software with Data Lake](https://www.g2.com/categories/big-data-analytics/f/data-lake) ## What Are the Top-Rated Big Data Analytics Software Products in 2026? ### 1. [ShareInsights](https://www.g2.com/products/shareinsights/reviews) Accelerite Share Insights is an end- to- end big data analytics platform that unifies different analytics operations like data processing, storage and visualization, It offer unique advantages like analytics development, managed life-cycle of analytics and future proofing. **Average Rating:** 4.3/5.0 **Total Reviews:** 12 **How Do G2 Users Rate ShareInsights?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.9/10 (Category avg: 8.5/10) - **Data Workflow:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind ShareInsights?** - **Seller:** [Accelerite](https://www.g2.com/sellers/accelerite) - **Year Founded:** 2014 - **HQ Location:** Santa Clara, CA - **Twitter:** @Accelerite (1,087 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/5118410 (18 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 42% Mid-Market, 33% Enterprise #### What Are Recent G2 Reviews of ShareInsights? **"[Very efficient tool for Big data analysis](https://www.g2.com/survey_responses/shareinsights-review-9308319)"** **Rating:** 4.5/5.0 stars *— Vanshika G.* [Read full review](https://www.g2.com/survey_responses/shareinsights-review-9308319) --- **"[Insight overview](https://www.g2.com/survey_responses/shareinsights-review-9336861)"** **Rating:** 4.5/5.0 stars *— Verified User in Education Management* [Read full review](https://www.g2.com/survey_responses/shareinsights-review-9336861) --- ### 2. [Apache Pig](https://www.g2.com/products/apache-pig/reviews) Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. **Average Rating:** 3.9/5.0 **Total Reviews:** 20 **How Do G2 Users Rate Apache Pig?** - **Has the product been a good partner in doing business?:** 7.5/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 7.8/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.3/10 (Category avg: 8.5/10) - **Data Workflow:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind Apache Pig?** - **Seller:** [The Apache Software Foundation](https://www.g2.com/sellers/the-apache-software-foundation) - **Year Founded:** 1999 - **HQ Location:** Wakefield, MA - **Twitter:** @TheASF (66,168 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/215982/ (2,408 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Internet - **Company Size:** 62% Enterprise, 19% Mid-Market #### What Are Recent G2 Reviews of Apache Pig? **"[Apache Pig makes it easy to create efficient data pipelines](https://www.g2.com/survey_responses/apache-pig-review-4154116)"** **Rating:** 4.5/5.0 stars *— Prashant V.* [Read full review](https://www.g2.com/survey_responses/apache-pig-review-4154116) --- **"[Apache Pig has saved my life from forever coding!](https://www.g2.com/survey_responses/apache-pig-review-4195334)"** **Rating:** 4.5/5.0 stars *— Prashanth K.* [Read full review](https://www.g2.com/survey_responses/apache-pig-review-4195334) --- ### 3. [Tinybird](https://www.g2.com/products/tinybird/reviews) Tinybird is a fully managed ClickHouse® service designed for software developers and AI-native product teams by enabling them to create large-scale real-time analytics projects with minimal effort. Tinybird makes integrating the open source ClickHouse database into applications simpler, faster, and more reliable, allowing engineers to focus on feature development rather than infrastructure management. Tinybird eliminates the complexities associated with traditional database management, making it an ideal choice for teams looking to leverage the power of ClickHouse without the overhead of server maintenance and scaling concerns. The target audience for Tinybird includes software developers, data engineers, technical founders, and AI-native product teams building real-time analytics capabilities in their applications. With the increasing demand for real-time data processing, Tinybird caters to teams that need to deliver insights quickly and efficiently. Use cases for Tinybird span various industries, including SaaS, e-commerce, finance, crypto, AI, and IoT, where real-time data analysis is crucial for decision-making and operational efficiency. By providing a managed service, Tinybird allows software engineers to deploy analytics features in days rather than months, significantly accelerating project timelines. Key features of Tinybird include a hosted ClickHouse database plus managed data ingestion and API layers, which simplify the process of integrating analytics into applications. The built-in authentication tools enhance security and data privacy, with support for row-level access policies using JWTs. Free observability logs storage and querying allow users to keep tabs on usage and performance. AI-native features, including Tinybird Code - a CLI agent with deep ClickHouse expertise - plus the Tinybird MCP Server, make integrating analytics features into LLM apps simpler and more robust. Additionally, Tinybird's architecture is designed to handle scaling automatically, allowing teams to focus on their core development tasks without worrying about understanding a new database or worrying about infrastructure details. For those who desire infrastructure control, Tinybird offers self-managed deployment, for free. This unique combination of features enables users to ship data-driven features rapidly while maintaining high performance and reliability. Tinybird stands out in the real-time analytics database landscape by providing the performance of one of the world's fastest OLAP databases without the associated complexity. By abstracting the technical challenges of managing clusters and provisioning resources, Tinybird empowers teams to innovate and iterate on their products more quickly. The service's emphasis on ease of use and rapid deployment makes it an attractive option for organizations looking to harness the power of real-time analytics without the burden of extensive operational overhead. With Tinybird, users can unlock the potential of their data and drive impactful insights, all while enjoying a seamless and efficient development experience. **Average Rating:** 4.1/5.0 **Total Reviews:** 14 **How Do G2 Users Rate Tinybird?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.6/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.3/10 (Category avg: 8.5/10) - **Data Workflow:** 9.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Tinybird?** - **Seller:** [Tinybird](https://www.g2.com/sellers/tinybird) - **Company Website:** https://tinybird.co - **Year Founded:** 2019 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/35704741 (51 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 50% Mid-Market, 36% Small-Business #### What Are Tinybird's Pros and Cons? **Pros:** - Ease of Use (6 reviews) - Analytics (4 reviews) - Easy Integrations (4 reviews) - Features (4 reviews) - Integrations (4 reviews) **Cons:** - Poor Customer Support (3 reviews) - Lack of Features (2 reviews) - Learning Curve (2 reviews) - Learning Difficulty (2 reviews) - Limited Customization (2 reviews) ### What Do G2 Reviewers Say About Tinybird? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** in Tinybird, facilitating quick integrations and seamless development of real-time analytics. - Users value Tinybird for its **accurate real-time analytics** , simplifying integration and decision-making processes. - Users value the **easy integrations** with other apps, enhancing the speed and efficiency of real-time analytics development. - Users praise Tinybird for its **ease of integration and real-time analytics** , enhancing developer efficiency and product capabilities. - Users value the **easy integrations** with apps like Confluent Cloud, enhancing real-time analytics and product development. **Cons:** - Users report **poor customer support** , with delays in response times and inadequate documentation for new users. - Users note a **lack of features** in Tinybird, limiting integration and customization options for various business needs. - Users struggle with the **steep learning curve** of Tinybird, finding it challenging to navigate and utilize effectively. - Users find that **learning difficulty** hampers their experience, especially due to the complex interface and inadequate documentation. - Users experience **limited customization** with Tinybird, hindering data flow and adaptability for varying business requirements. #### What Are Recent G2 Reviews of Tinybird? **"[Best tool for building dashboards and develop low latency APIs](https://www.g2.com/survey_responses/tinybird-review-9121969)"** **Rating:** 4.0/5.0 stars *— Maxwel S.* [Read full review](https://www.g2.com/survey_responses/tinybird-review-9121969) --- **"[Building real-time analytics made simple with Tinybird](https://www.g2.com/survey_responses/tinybird-review-9122091)"** **Rating:** 4.5/5.0 stars *— Bhavya J.* [Read full review](https://www.g2.com/survey_responses/tinybird-review-9122091) --- #### What Are G2 Users Discussing About Tinybird? - [What is Tinybird used for?](https://www.g2.com/discussions/what-is-tinybird-used-for) ### 4. [StarTree](https://www.g2.com/products/startree/reviews) StarTree Cloud is a fully-managed user-facing real-time analytics Database-as-a-Service (DBaaS) designed for OLAP at massive speed and scale. Based on Apache Pinot™, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. **Average Rating:** 4.5/5.0 **Total Reviews:** 29 **How Do G2 Users Rate StarTree?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.8/10 (Category avg: 8.5/10) - **Data Workflow:** 7.9/10 (Category avg: 8.5/10) **Who Is the Company Behind StarTree?** - **Seller:** [StarTree](https://www.g2.com/sellers/startree) - **Company Website:** https://www.startree.ai/ - **Year Founded:** 2019 - **HQ Location:** Mountain View, California - **Twitter:** @startreedata (2,275 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/startreedata/ (118 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Financial Services - **Company Size:** 38% Small-Business, 31% Enterprise #### What Are StarTree's Pros and Cons? **Pros:** - Performance (4 reviews) - Analytics (3 reviews) - Ease of Use (3 reviews) - Fast Querying (3 reviews) - Interface Ease-of-Use (3 reviews) **Cons:** - Learning Curve (3 reviews) - Complex Setup (2 reviews) - Difficult Setup (2 reviews) - Insufficient Documentation (2 reviews) - Poor Documentation (2 reviews) ### What Do G2 Reviewers Say About StarTree? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **fast real-time analytics** of StarTree, appreciating its efficiency and reliability for large datasets. - Users love the **fast real-time analytics** of StarTree, enabling quick insights and efficient data exploration at scale. - Users appreciate the **ease of use** of StarTree, enjoying intuitive navigation and efficient onboarding for analytics tasks. - Users commend StarTree for its **extremely fast querying** , enabling efficient real-time analytics on large datasets effortlessly. - Users praise the **intuitive interface** of StarTree, making real-time analytics exploration easy and efficient for all. **Cons:** - Users find the **learning curve steep** initially, especially for tuning and configuring StarTree effectively. - Users find the **complex setup** challenging, particularly for new users needing to configure performance and pipelines. - Users find the **difficult setup** challenging, especially for beginners and with complex integrations, impacting initial experience. - Users find the **insufficient documentation** challenging, especially for complex configurations and specific use cases. - Users face issues with **poor documentation** , finding it lacking in detail and clarity for configuration and setup. #### What Are Recent G2 Reviews of StarTree? **"[Powerful Real-Time Analytics with Intuitive Interface and Seamless Scalability](https://www.g2.com/survey_responses/startree-review-11958223)"** **Rating:** 4.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/startree-review-11958223) --- **"[Blazing-Fast Real-Time Analytics with Intuitive UI](https://www.g2.com/survey_responses/startree-review-11977802)"** **Rating:** 5.0/5.0 stars *— Verified User in Graphic Design* [Read full review](https://www.g2.com/survey_responses/startree-review-11977802) --- ### 5. [Gathr.ai](https://www.g2.com/products/gathr-ai/reviews) Gathr.ai powers AI with complete data context for higher quality intelligence. With day-zero, high-fidelity data discourse, users can get data-backed answers to the ‘why’, ‘what-if’, and ‘how do I’ questions that drive business KPIs forward. This intelligence is delivered natively on top of the organization’s existing data estate — including data warehouses, databases, federated SQL engines, and operational systems. Leading businesses across industries also rely on Gathr.ai to build high-performance data pipelines, bespoke Data+AI solutions, and action-driven analytics experiences. Built for builders, Gathr.ai delivers agility, performance, and control. It snaps into the existing stack — integrating upstream and downstream systems with no extra plumbing. It gives developers starter-kit speed and full extension freedom. **Average Rating:** 4.8/5.0 **Total Reviews:** 33 **How Do G2 Users Rate Gathr.ai?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.8/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Gathr.ai?** - **Seller:** [Gathr.ai](https://www.g2.com/sellers/gathr-ai) - **Year Founded:** 2022 - **HQ Location:** Los Gatos, CA, US - **LinkedIn® Page:** https://www.linkedin.com/company/gathr-one (57 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Associate Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 79% Mid-Market, 21% Enterprise #### What Are Gathr.ai's Pros and Cons? **Pros:** - Integrations (9 reviews) - Data Management (7 reviews) - Drag (6 reviews) - Ease of Use (6 reviews) - Easy Integrations (6 reviews) **Cons:** - Access Issues (1 reviews) - Connection Issues (1 reviews) - Difficult Setup (1 reviews) - Lack of Real-Time Data (1 reviews) - Performance Optimization (1 reviews) ### What Do G2 Reviewers Say About Gathr.ai? *AI-generated summary from verified user reviews* **Pros:** - Users love the **seamless integrations** of Gathr.ai, enabling swift and efficient AI-driven analytics with minimal effort. - Users rave about Gathr.ai's **data management capabilities** , enabling seamless data exploration and instant, insightful responses. - Users appreciate the **intuitive drag-and-drop interface** of Gathr.ai, enabling swift creation of complex data pipelines. - Users value the **ease of use** of Gathr.ai, enabling quick data integration with its low-code, drag-and-drop interface. - Users value the **easy integrations** of Gathr.ai, enabling quick setup and seamless data pipeline configurations. **Cons:** - Users face **access issues** due to the need for more native connectors, which affects their experience with Gathr.ai. - Users experienced **connection issues** with legacy systems initially, though support offered timely workarounds and updates. - Users note that the **difficult setup** of custom connectors may require extra effort, though it's not a major drawback. - Users find the **lack of real-time data** challenging for monitoring and improving pipeline performance effectively. - Users find the **performance optimization** aspect of Gathr.ai requires technical knowledge, complicating data transfer monitoring. #### What Are Recent G2 Reviews of Gathr.ai? **"[Enables deep, self-service data exploration](https://www.g2.com/survey_responses/gathr-ai-review-11440931)"** **Rating:** 5.0/5.0 stars *— Neha G.* [Read full review](https://www.g2.com/survey_responses/gathr-ai-review-11440931) --- **"[Simplified data transformation for GenAI success](https://www.g2.com/survey_responses/gathr-ai-review-11262615)"** **Rating:** 4.5/5.0 stars *— siddharth g.* [Read full review](https://www.g2.com/survey_responses/gathr-ai-review-11262615) --- ### 6. [Plotly Dash Enterprise](https://www.g2.com/products/plotly-dash-enterprise/reviews) Dash is the trusted solution for operationalizing Python models, allowing data science teams to focus on data and models, while still producing and deploying enterprise-ready apps. What would typically require a team of back-end developers, front-end developers and IT can all be done with Dash. It enables data science teams to build, design, deploy, and securely manage data-driven applications that align with your business goals. Companies can deliver on their data, analytic, and AI initiatives quickly and effectively -- no JavaScript, CSS, CronJobs or DevOps required. **Average Rating:** 4.8/5.0 **Total Reviews:** 36 **How Do G2 Users Rate Plotly Dash Enterprise?** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10) **Who Is the Company Behind Plotly Dash Enterprise?** - **Seller:** [Plotly](https://www.g2.com/sellers/plotly) - **Year Founded:** 2013 - **HQ Location:** Montréal, CA - **Twitter:** @plotlygraphs (41,308 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3327684/ (106 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software - **Company Size:** 47% Small-Business, 31% Enterprise #### What Are Plotly Dash Enterprise's Pros and Cons? **Pros:** - Charting Features (1 reviews) - Coding Ease (1 reviews) - Customer Support (1 reviews) - Dashboard Management (1 reviews) - Data Visualization (1 reviews) ### What Do G2 Reviewers Say About Plotly Dash Enterprise? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **easy-to-use charting features** of Plotly Dash Enterprise, enabling beautiful visualizations without coding. - Users value the **coding ease** of Plotly Dash Enterprise, allowing for seamless creation of visualizations effortlessly. - Users value the **exceptional customer support** from Plotly Dash Enterprise, enhancing their experience and satisfaction significantly. - Users value the **easy dashboard management** of Plotly Dash Enterprise, enabling efficient and beautiful visualizations effortlessly. - Users appreciate the **easy and beautiful data visualizations** in Plotly Dash Enterprise, enhancing their reporting capabilities effortlessly. #### What Are Recent G2 Reviews of Plotly Dash Enterprise? **"[Its the perfect Data Management](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-10698270)"** **Rating:** 4.5/5.0 stars *— Maria F.* [Read full review](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-10698270) --- **"[Dash Dominance in Creating Interactive Web Apps for Your Data Insights](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-8889756)"** **Rating:** 5.0/5.0 stars *— Uttam M.* [Read full review](https://www.g2.com/survey_responses/plotly-dash-enterprise-review-8889756) --- #### What Are G2 Users Discussing About Plotly Dash Enterprise? - [What are 3 benefits of a dashboard?](https://www.g2.com/discussions/dash-what-are-3-benefits-of-a-dashboard) - [What is the best dashboard software?](https://www.g2.com/discussions/dash-what-is-the-best-dashboard-software) - [How good is Dash?](https://www.g2.com/discussions/how-good-is-dash) - [What is the function of a dashboard?](https://www.g2.com/discussions/what-is-the-function-of-a-dashboard) ### 7. [Strategy Mosaic](https://www.g2.com/products/strategy-mosaic/reviews) Strategy Mosaic, from Strategy (formerly MicroStrategy), is an enterprise-grade universal semantic layer solution designed to enhance the capabilities of AI and Business Intelligence (BI) within organizations. It addresses critical challenges such as data fragmentation and inconsistent metrics, which lead to untrusted AI answers, compliance risks, and runaway cloud costs. The universal semantic layer that Mosaic provides serves as a centralized repository for business definitions, hierarchies, and security rules, ensuring that all users access consistent metrics and KPIs regardless of the tools they employ. This single source of truth is actively monitored by our integrated Sentinel layer, which moves you from reactive audits to proactive, real-time governance. Sentinel provides immediate intelligence on potential data breaches, compliance risks, and cost-saving opportunities, helping you optimize cloud spend and prevent violations before they happen. Additionally, Mosaic empowers organizations to build an auditable foundation for AI. By providing a layer of rich business context and consistent, human-readable definitions, Mosaic gives AI models the deep understanding required to provide more accurate and verifiable answers. This accelerates time to insight, allows you to end vendor lock-in, and dramatically reduces the total cost of ownership (TCO) by eliminating costly data rework and optimizing data management processes. In summary, Strategy Mosaic stands out by addressing the fundamental issues of data fragmentation and governance. Its robust connectivity, centralized semantic layer, and focus on delivering trusted data make it an invaluable tool for organizations aiming to enhance their analytics capabilities and leverage AI effectively. **Average Rating:** 4.5/5.0 **Total Reviews:** 15 **How Do G2 Users Rate Strategy Mosaic?** - **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.2/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Strategy Mosaic?** - **Seller:** [Strategy (formerly MicroStrategy)](https://www.g2.com/sellers/strategy-formerly-microstrategy) - **Company Website:** https://www.strategy.com/software - **Year Founded:** 1989 - **HQ Location:** Tysons Corner, VA - **Twitter:** @MicroStrategy (303,456 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/strategy/ (3,457 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 53% Enterprise, 40% Mid-Market #### What Are Strategy Mosaic's Pros and Cons? **Pros:** - Ease of Use (2 reviews) - Features (2 reviews) - Reporting (2 reviews) - Data Analysis (1 reviews) - Data Modeling (1 reviews) **Cons:** - Bugs (2 reviews) - Bug Issues (1 reviews) - Debugging Issues (1 reviews) - Expensive (1 reviews) - Learning Curve (1 reviews) ### What Do G2 Reviewers Say About Strategy Mosaic? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **ease of use** of Strategy Mosaic, enjoying effortless collaboration and structured planning with minimal hassle. - Users appreciate the **clear visual roadmap** Strategy Mosaic provides, facilitating effortless collaboration and structured planning. - Users value the **user-friendly reporting** of Strategy Mosaic, appreciating its semantic layer and reliable data analytics. - Users value the **robust data analysis** capabilities of Strategy Mosaic, ensuring reliable and user-friendly insights. - Users value the **auto-magical experience** of crafting their initial data model effortlessly with Strategy Mosaic. **Cons:** - Users experience **minor bugs** with Strategy Mosaic, including issues with cube publishing that hinder business efficiency. - Users often face **bug issues** with Strategy Mosaic, where corrections fail to update the code as intended. - Users struggle with **debugging issues** in Strategy Mosaic, where corrections don't apply as expected, leading to frustration. - Users suggest that the **license cost is high** and recommend exploring options for better pricing. - Users find the **initial learning curve** of Strategy Mosaic to be overwhelming, making it difficult for newcomers to adapt. #### What Are Recent G2 Reviews of Strategy Mosaic? **"[Strategy Mosaic Makes Strategic Planning Clear, Visual, and Team-Aligned](https://www.g2.com/survey_responses/strategy-mosaic-review-12611240)"** **Rating:** 4.0/5.0 stars *— Akash A.* [Read full review](https://www.g2.com/survey_responses/strategy-mosaic-review-12611240) --- **"[Effortless Planning and Team Alignment Made Simple](https://www.g2.com/survey_responses/strategy-mosaic-review-12008071)"** **Rating:** 4.5/5.0 stars *— Aditya K.* [Read full review](https://www.g2.com/survey_responses/strategy-mosaic-review-12008071) --- ### 8. [Megaladata](https://www.g2.com/products/megaladata/reviews) The low code Megaladata platform empowers business users by making advanced analytics accessible. - Visual design of complex data analysis models with no involvement of the IT department and no need for programming. - Over 60 ready-to-use processing components. - Easy integration with various sources. - Fast processing of large datasets achieved through in-memory computing and parallelism. - Reusable components that facilitate accumulation of business expertise. - Advanced visualization — OLAP cubes, tables, charts, and other specialized tools. Megaladata minimizes the time between hypothesis testing and a fully functional business process. **Average Rating:** 4.9/5.0 **Total Reviews:** 8 **How Do G2 Users Rate Megaladata?** - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind Megaladata?** - **Seller:** [Megaladata](https://www.g2.com/sellers/megaladata) - **HQ Location:** Neu-Isenburg, DE - **Twitter:** @megaladata_com (5 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/megaladata (5 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 88% Small-Business, 13% Mid-Market #### What Are Recent G2 Reviews of Megaladata? **"[Reusable Components and Nice Performance](https://www.g2.com/survey_responses/megaladata-review-12780960)"** **Rating:** 5.0/5.0 stars *— Vitali M.* [Read full review](https://www.g2.com/survey_responses/megaladata-review-12780960) --- **"[Megaladata is a platform for any time of on-time analytics](https://www.g2.com/survey_responses/megaladata-review-12752778)"** **Rating:** 5.0/5.0 stars *— Vartan G.* [Read full review](https://www.g2.com/survey_responses/megaladata-review-12752778) --- ### 9. [Savant Labs](https://www.g2.com/products/savant-labs/reviews) Savant is an AI automation platform built for enterprise finance, tax, and accounting teams. It turns messy, manual data work like extraction, preparation, reconciliation, and reporting into centrally governed workflows, so teams can be more efficient without sacrificing accuracy, control, or compliance. Trusted by Fortune 500 enterprises, Savant catches errors before they're filed, ensures audit readiness without the scramble, and gives finance teams their time back. WHAT SETS SAVANT APART Unlike general-purpose AI tools or legacy analytics platforms, Savant was built specifically for finance workflows where 99% accuracy isn't good enough — because 1% errors at scale become audit findings, restatements, and compliance exposure. Three things make Savant different - Deterministic, not probabilistic: Savant uses rule-based AI agents, not LLM guesses. Consistent inputs produce consistent outputs. - Governance is built in, not bolted on: Audit trail, data lineage, SOX controls, and role-based access are standard, not add-ons. - Handles the data other tools can't: Native processing for PDFs, scanned documents, and invoices — the unstructured data that breaks legacy workflows. KEY FEATURES - AI-powered data automation: Automate any data task end to end — prep, blending, transformation, publishing, and alerting. Works with structured and unstructured data, including PDFs, scanned documents, and ERP extracts. - Deterministic workflow engine: AI agents follow step-by-step logic with validation at each stage. Same inputs produce the same outputs, every time — no black boxes, no probabilistic guesses. - Built-in audit trail and data lineage: Every workflow step is logged automatically. Complete data lineage from source to output. No manual documentation, no reconstructing steps across email chains. - SOX compliance by design: Segregation of duties, version control, approval management, and user activity history are built in from day one. - Human-in-the-loop exception handling: Savant proactively flags exceptions for human review, allowing analysts to catch errors before they reach a filing. The AI learns from human judgments over time. - 500+ enterprise connectors: Connect to your existing ERPs, CRMs, BI platforms, file systems, email, and more out of the box. - User-friendly interface: No SQL, no code, no IT tickets. If your team can use Excel, they can build and run workflows in Savant. - Enterprise-grade security: SOC 2 Type II, SOC 1 Type II, ISO 27001. SSO/SAML, role-based access control, private cloud and VPC deployment available. USE CASES - Month-end and year-end close automation - Financial reconciliations and tie-outs - Tax provision preparation - State apportionment calculations - Sales and use tax reconciliation - Data extraction from PDFs, invoices, and scanned documents - ERP data consolidation across multiple systems - Intercompany accounting and multi-entity reporting - Audit evidence package preparation - Recurring reporting and dashboard publishing **Average Rating:** 4.7/5.0 **Total Reviews:** 50 **How Do G2 Users Rate Savant Labs?** - **Has the product been a good partner in doing business?:** 9.8/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.2/10 (Category avg: 8.5/10) - **Data Workflow:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Savant Labs?** - **Seller:** [Savant Labs](https://www.g2.com/sellers/savant-labs) - **Company Website:** https://www.savantlabs.io - **Year Founded:** 2021 - **HQ Location:** San Francisco, California - **LinkedIn® Page:** https://www.linkedin.com/company/savant-labs (61 employees on LinkedIn®) **Who Uses This Product?** - **Top Industries:** Computer Software, Logistics and Supply Chain - **Company Size:** 43% Enterprise, 39% Mid-Market #### What Are Savant Labs's Pros and Cons? **Pros:** - Ease of Use (14 reviews) - Customer Support (9 reviews) - User Interface (7 reviews) - Integrations (6 reviews) - Scalability (6 reviews) **Cons:** - Learning Curve (7 reviews) - Learning Difficulty (6 reviews) - Integration Issues (3 reviews) - Access Issues (2 reviews) - Data Management (2 reviews) ### What Do G2 Reviewers Say About Savant Labs? *AI-generated summary from verified user reviews* **Pros:** - Users find Savant Labs' **ease of use** invaluable, simplifying data analysis without the need for coding skills. - Users praise the **extremely positive and speedy customer support** of Savant Labs, enhancing their overall experience and satisfaction. - Users love the **intuitive and user-friendly interface** of Savant Labs, making data analytics accessible and efficient. - Users love the **easy integration** of Savant Labs, simplifying data connectivity with seamless setup and support. - Users value the **scalability** of Savant Labs, finding it easy to adapt and automate analyses with minimal effort. **Cons:** - Users find the **unique learning curve** of Savant Labs challenging, requiring practice and adaptation to master the platform. - Users note a **steep learning curve** for Savant Labs, requiring practice and resources to achieve fluency. - Users face **integration issues** with Savant Labs, struggling with data updates and tool compatibility, complicating analyses. - Users often face **access issues** with Savant Labs, struggling to log in and navigate complex features effectively. - Users experience **data management difficulties** with updates and refreshes, impacting workflow efficiency in Savant Labs. #### What Are Recent G2 Reviews of Savant Labs? **"[Faster close + auditors actually trust our process now](https://www.g2.com/survey_responses/savant-labs-review-12941283)"** **Rating:** 5.0/5.0 stars *— Lawrence M.* [Read full review](https://www.g2.com/survey_responses/savant-labs-review-12941283) --- **"[Automated sales tax reconciliation and significantly shortened month-end close](https://www.g2.com/survey_responses/savant-labs-review-12942104)"** **Rating:** 5.0/5.0 stars *— Marlene S.* [Read full review](https://www.g2.com/survey_responses/savant-labs-review-12942104) --- ### 10. [Datacoves](https://www.g2.com/products/datacoves/reviews) Datacoves is an enterprise DataOps platform with managed dbt Core and Airflow for data transformation and orchestration. We offer VS Code in the browser for dbt development with the ability to include preferred VS Code extensions and Python libraries such as the official Snowflake Extension and Snowpark. You may also optionally use our managed Airbyte and Superset for a full end-to-end solution. **Average Rating:** 4.8/5.0 **Total Reviews:** 18 **How Do G2 Users Rate Datacoves?** - **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.0/10 (Category avg: 8.5/10) - **Data Workflow:** 8.7/10 (Category avg: 8.5/10) **Who Is the Company Behind Datacoves?** - **Seller:** [Datacoves Inc](https://www.g2.com/sellers/datacoves-inc) - **Year Founded:** 2021 - **HQ Location:** Thousand Oaks, California - **Twitter:** @datacoves (475 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/datacoves/ (12 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 44% Enterprise, 28% Mid-Market #### What Are Datacoves's Pros and Cons? **Pros:** - Ease of Use (6 reviews) - Features (5 reviews) - Integrations (5 reviews) - Data Engineering (4 reviews) - Easy Integrations (4 reviews) **Cons:** - Alert Overload (1 reviews) - Dashboard Issues (1 reviews) - Data Limitations (1 reviews) - Dependency Issues (1 reviews) - Difficult Learning (1 reviews) ### What Do G2 Reviewers Say About Datacoves? *AI-generated summary from verified user reviews* **Pros:** - Users value the **ease of use** of Datacoves, appreciating its quick setup and responsive support during implementation. - Users appreciate the **well-thought-out development and deployment system** of Datacoves, enhancing efficiency and scaling teams effortlessly. - Users value the **seamless integrations** of Datacoves that enhance collaboration and streamline data processes effectively. - Users value the **exceptional data engineering tools** in Datacoves that enhance collaboration and improve data quality significantly. - Users love the **API integration** of Datacoves, enhancing their workflow seamlessly within VS Code. **Cons:** - Users express concerns about **alert overload** , highlighting the need for better monitoring to manage support requests effectively. - Users express the need for better **dashboard integrations** to monitor activities and troubleshoot performance issues effectively. - Users may find the **data limitations** restrictive, as they feel locked into specific ELT tools. - Users note **dependency issues** with Datacoves, feeling locked into specific ELT tools that may not suit everyone. - Users experience **integration issues** due to a lack of dashboards, complicating monitoring and support during failures. #### What Are Recent G2 Reviews of Datacoves? **"[Great Developer Experience with Responsive Support](https://www.g2.com/survey_responses/datacoves-review-12882735)"** **Rating:** 5.0/5.0 stars *— Anthony L.* [Read full review](https://www.g2.com/survey_responses/datacoves-review-12882735) --- **"[Datacoves Simplifies Our Data Stack with a Smooth, Customizable Dev Experience](https://www.g2.com/survey_responses/datacoves-review-12582185)"** **Rating:** 5.0/5.0 stars *— Sung Hoon J.* [Read full review](https://www.g2.com/survey_responses/datacoves-review-12582185) --- ### 11. [Keboola](https://www.g2.com/products/keboola/reviews) Keboola is the unified AI & Data orchestration platform that empowers organizations to turn data into business value faster and more securely than ever. It acts as your agentic AI co-pilot for data workflows, automating everything from integration to insight. With Keboola, Engineering teams, digital natives, startup CTOs, and innovation leads alike can rapidly build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. Built for Every Data Persona: Whether you’re a seasoned data engineer or a business analyst, Keboola is built to make you successful. Data engineers love the open extensibility – code in SQL, Python, R, or use our API/CLI to tailor any step. Analysts and non-coders love the self-service UI – point-and-click data pipeline assembly, drag-and-drop transformations with text to SQL on semantic layer, and one-click deployment of pre-built workflows. Collaboration is seamless, with shared workspaces and sandboxes that let teams build and share data products freely without affecting production. What sets us apart? With Keboola, you can build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. 🔗 Unified Connectivity: Effortlessly connect to 700+ data sources (databases, SaaS apps, and APIs) .Real-time Streams, Change Data Capture or batch. 🤖 Agentic AI Orchestration: Keboola’s AI-driven engine orchestrates data pipelines and ML workflows automatically. It can trigger the next steps based on data events or quality checks, and dynamically allocate resources. Think of it as an autopilot for your data & AI, ensuring pipelines run optimally and recover on their own from hiccups. 🛡️ Built-in Governance & Security: Every dataset and process in Keboola is governed. Fine-grained access controls, lineage tracking, and audit logs are native to the platform. Compliance is simplified – SOC 2, GDPR, and industry standards are supported out-of-the-box. 🚀 Rapid Development & Prototyping: Innovate without constraints. Spin up isolated dev/test sandboxes in seconds to prototype new data products or AI models. 🌎 Multi-Cloud Scalability: Built on a cloud-native architecture, Keboola scales with your needs. Deploy on your preferred cloud (AWS, Azure, GCP) and let Keboola handle the heavy lifting – elastic compute, parallel processing, and workload optimization. Start small and scale to enterprise workloads globally, without re-architecting. 💡 End-to-End Insight Activation: Because Keboola unifies your data pipelines, analytics, and ML, you can go from raw data to AI-driven insights in record time. Why Keboola: Instead of cobbling together multiple tools for integration, ETL/ELT, data catalogs, automation, and AI, Keboola delivers a single platform that does it all – with unprecedented ease and intelligence. Our customers have replaced 5-10 disparate tools with Keboola’s unified solution, drastically accelerating delivery. Join 30,000+ companies and industry leaders who use Keboola to supercharge their data teams. Whether you need to deliver data to AI Agents, streamline a complex data estate, or build and share data products to business, Keboola’s AI orchestration platform adapts to your needs – freeing you to focus on innovation and business growth. **Average Rating:** 4.6/5.0 **Total Reviews:** 133 **How Do G2 Users Rate Keboola?** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 5.0/10 (Category avg: 8.5/10) - **Data Workflow:** 9.2/10 (Category avg: 8.5/10) **Who Is the Company Behind Keboola?** - **Seller:** [Keboola](https://www.g2.com/sellers/keboola) - **Company Website:** https://www.keboola.com - **Year Founded:** 2008 - **HQ Location:** Prague - **Twitter:** @keboola (2,004 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/keboola/ (97 employees on LinkedIn®) **Who Uses This Product?** - **Who Uses This:** Data Analyst, Data Engineer - **Top Industries:** Information Technology and Services, Marketing and Advertising - **Company Size:** 64% Mid-Market, 21% Small-Business #### What Are Keboola's Pros and Cons? **Pros:** - Ease of Use (35 reviews) - Features (27 reviews) - Data Management (26 reviews) - Integrations (26 reviews) - Customer Support (25 reviews) **Cons:** - Learning Curve (14 reviews) - Complexity (13 reviews) - Steep Learning Curve (11 reviews) - Data Management (9 reviews) - UX Improvement (9 reviews) ### What Do G2 Reviewers Say About Keboola? *AI-generated summary from verified user reviews* **Pros:** - Users praise the **ease of use** of Keboola, making data management accessible for less technical users. - Users love the **ease of data management** in Keboola, enjoying the growing number of connectors and excellent support. - Users enjoy the **intuitive data flow setup** in Keboola, enhancing productivity and simplifying diverse data management. - Users value the **extensive connectors** in Keboola, enhancing data management flexibility and integration ease. - Users appreciate the **incredible customer support** from Keboola, noting their fast and helpful assistance for all queries. **Cons:** - Users find that the **learning curve is steep** , often needing developer assistance to navigate connections and components. - Users face a **steep learning curve** with Keboola, finding the DAG and documentation challenging to navigate and understand. - Users face a **steep learning curve** with Keboola, often needing support due to complex setups and insufficient resources. - Users face challenges with the **difficult interface and data handling** , leading to complications in effective data management. - Users find the **user interface non-intuitive** and overwhelming, making navigation and onboarding challenging for newcomers. #### What Are Recent G2 Reviews of Keboola? **"[Effortless Data Management with Stellar Support](https://www.g2.com/survey_responses/keboola-review-11930748)"** **Rating:** 4.5/5.0 stars *— Pavel S.* [Read full review](https://www.g2.com/survey_responses/keboola-review-11930748) --- **"[Streamlines Data Prep with Some Learning Curves](https://www.g2.com/survey_responses/keboola-review-9741142)"** **Rating:** 5.0/5.0 stars *— Vojta F.* [Read full review](https://www.g2.com/survey_responses/keboola-review-9741142) --- #### What Are G2 Users Discussing About Keboola? - [What are the benefits and challenges of using Keboola for data integration, and what do you recommend for new users?](https://www.g2.com/discussions/what-are-the-benefits-and-challenges-of-using-keboola-for-data-integration-and-what-do-you-recommend-for-new-users) ### 12. [Deep.BI](https://www.g2.com/products/deep-bi/reviews) Deep.BI measures content consumption metrics and provides user engagement scoring to power publisher's content delivery, marketing tools and paywalls to grow, engage and retain audiences. Deep.BI collects all kinds of raw event data related to publishing, like reader’s behavior and content performance, and analyzes this data in real-time (sub-second latency between ingestion and data visualization). By collecting first-party raw data (no sampling & no aggregation), publishers get unprecedented flexibility in building their own metrics, reports, and different strategies for different kinds of content. This also allows publishers to quickly test hypotheses on both live and historical data. These dashboards and reports are shareable and customizable across teams making the workload on the analysts much lighter and gives them the ability to deliver what they want to deliver in the way they want and in lightning speeds! **Average Rating:** 4.4/5.0 **Total Reviews:** 10 **How Do G2 Users Rate Deep.BI?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 7.5/10 (Category avg: 8.5/10) - **Data Workflow:** 7.5/10 (Category avg: 8.5/10) **Who Is the Company Behind Deep.BI?** - **Seller:** [Deep.BI](https://www.g2.com/sellers/deep-bi) - **Year Founded:** 2016 - **HQ Location:** San Francisco, California - **Twitter:** @_DeepBI (962 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/deep-bi/ (18 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Small-Business, 40% Mid-Market #### What Are Deep.BI's Pros and Cons? **Pros:** - Analytics (2 reviews) - Insights (2 reviews) - Insights Generation (2 reviews) - Audience Engagement (1 reviews) - Automation (1 reviews) **Cons:** - Coding Difficulty (1 reviews) - Confusing Interface (1 reviews) - Not Intuitive (1 reviews) - Poor Interface Design (1 reviews) - Poor UI Design (1 reviews) ### What Do G2 Reviewers Say About Deep.BI? *AI-generated summary from verified user reviews* **Pros:** - Users value the **real-time actionable insights** from Deep.BI, enhancing their ability to analyze data effectively. - Users appreciate the **real-time actionable insights** from Deep.BI, enhancing their ability to analyze customer interactions swiftly. - Users value the **real-time actionable insights** from Deep.BI, enabling efficient decision-making based on customer behavior. - Users value the **real-time actionable insights** from Deep.BI, enhancing customer interaction understanding and content performance. - Users value the **automation** capabilities of Deep.BI, streamlining the creation of BI dashboards efficiently. **Cons:** - Users find the **coding difficulty** of Deep.BI challenging, requiring technical skills for effective use. - Users find Deep.BI's interface **confusing for layman users** , making it difficult to navigate effectively. - Users feel the interface is **not intuitive** , making it challenging for layman users to navigate Deep.BI effectively. - Users find the **UI/UX lacking** , especially for those not familiar with advanced technical interfaces. - Users find the **UI/UX lacking** , particularly challenging for those unfamiliar with technology. #### What Are Recent G2 Reviews of Deep.BI? **"[Helpful real-time data processing](https://www.g2.com/survey_responses/deep-bi-review-10032368)"** **Rating:** 4.0/5.0 stars *— Frederic G.* [Read full review](https://www.g2.com/survey_responses/deep-bi-review-10032368) --- **"[Useful for processing data and assessing reports in real time](https://www.g2.com/survey_responses/deep-bi-review-10391757)"** **Rating:** 4.0/5.0 stars *— Komal R.* [Read full review](https://www.g2.com/survey_responses/deep-bi-review-10391757) --- #### What Are G2 Users Discussing About Deep.BI? - [What is the three 3 capabilities of BIS business intelligence system?](https://www.g2.com/discussions/what-is-the-three-3-capabilities-of-bis-business-intelligence-system) - [What are the functions of BI systems?](https://www.g2.com/discussions/what-are-the-functions-of-bi-systems) - [What are the key capabilities of BI?](https://www.g2.com/discussions/what-are-the-key-capabilities-of-bi) ### 13. [HyperAspect Cognitive Cloud](https://www.g2.com/products/hyperaspect-cognitive-cloud/reviews) HyperAspect Cognitive Cloud is an enterprise AI analytics and automation platform that empowers users to leverage big data to drive strategic, efficient decision-making across departments. We bring responsible AI and natural language processing into an organization's core processes with the required security compliance frameworks within data-intensive industries like healthcare, finance, insurance, legal, marketing, retail, professional digital services. **Average Rating:** 5.0/5.0 **Total Reviews:** 6 **How Do G2 Users Rate HyperAspect Cognitive Cloud?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.6/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind HyperAspect Cognitive Cloud?** - **Seller:** [HyperAspect](https://www.g2.com/sellers/hyperaspect) - **Year Founded:** 2017 - **HQ Location:** Washinghton , US - **LinkedIn® Page:** https://bg.linkedin.com/company/hyperaspect (11 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Mid-Market, 33% Small-Business #### What Are HyperAspect Cognitive Cloud's Pros and Cons? **Pros:** - AI Capabilities (3 reviews) - AI Integration (3 reviews) - Cloud Computing (3 reviews) - Customer Support (3 reviews) - Easy Integrations (3 reviews) **Cons:** - Expensive (1 reviews) - Pricing Issues (1 reviews) ### What Do G2 Reviewers Say About HyperAspect Cognitive Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users value the **AI-powered insights** of HyperAspect Cognitive Cloud, enhancing data analysis and cloud computing effectiveness. - Users find the **user-friendly AI integration** of HyperAspect Cognitive Cloud makes data analysis accessible for all skill levels. - Users highlight the **user-friendly design** of HyperAspect Cognitive Cloud, making AI and big data accessible for everyone. - Users find the **helpful customer support** of HyperAspect Cognitive Cloud essential for seamless implementation and enhanced productivity. - Users value the **easy integrations** of HyperAspect Cognitive Cloud, enhancing productivity with seamless setup for diverse applications. **Cons:** - Users find the pricing of HyperAspect Cognitive Cloud to be **a bit costly for smaller businesses** , limiting accessibility. - Users feel the **pricing may be a bit costly** for smaller businesses, impacting accessibility and affordability. #### What Are Recent G2 Reviews of HyperAspect Cognitive Cloud? **"[Very easy to work with](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10712175)"** **Rating:** 5.0/5.0 stars *— Atanas A.* [Read full review](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10712175) --- **"[Very user friendly and effective Cloud Computing solution](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10716137)"** **Rating:** 5.0/5.0 stars *— Viktor I.* [Read full review](https://www.g2.com/survey_responses/hyperaspect-cognitive-cloud-review-10716137) --- ### 14. [BellaDati](https://www.g2.com/products/belladati/reviews) Agile analytics and reporting tool, which enables business users to make informed decisions from real-time business data **Average Rating:** 3.5/5.0 **Total Reviews:** 4 **How Do G2 Users Rate BellaDati?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind BellaDati?** - **Seller:** [BellaDati](https://www.g2.com/sellers/belladati) - **Year Founded:** 2013 - **HQ Location:** Singapore, SG - **Twitter:** @BellaDati (291 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/belladati (14 employees on LinkedIn®) - **Phone:** 866-668-0180 **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Small-Business #### What Are Recent G2 Reviews of BellaDati? **"[Very powerful BI tool at a fraction of the cost.](https://www.g2.com/survey_responses/belladati-review-412356)"** **Rating:** 4.0/5.0 stars *— Verified User in Computer Software* [Read full review](https://www.g2.com/survey_responses/belladati-review-412356) --- **"[BellaDati IOT](https://www.g2.com/survey_responses/belladati-review-5320786)"** **Rating:** 5.0/5.0 stars *— Bobby Chakravarthy B.* [Read full review](https://www.g2.com/survey_responses/belladati-review-5320786) --- #### What Are G2 Users Discussing About BellaDati? - [What is BellaDati used for?](https://www.g2.com/discussions/what-is-belladati-used-for) ### 15. [BIRD Analytics](https://www.g2.com/products/bird-analytics/reviews) Lightning fast insights at business scale! BIRD Analytics platform provides real time insights on any data, be it batch data or data-in-motion. With its cloud native full-stack capabilities, in-built scalable enterprise data warehouse, 100+ out-of-box connectors with readymade KPI driven dashboards, ERP accelerators and streaming/event driven capabilities, be rest assured that your investments are secured on the right technology platform for the near future. **Average Rating:** 4.9/5.0 **Total Reviews:** 6 **How Do G2 Users Rate BIRD Analytics?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.4/10 (Category avg: 8.5/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind BIRD Analytics?** - **Seller:** [BirdAnalytics](https://www.g2.com/sellers/birdanalytics) - **Year Founded:** 2014 - **HQ Location:** Newark, US - **Twitter:** @BIRDanalytics (71 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/birdanalytics/about/ (17 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of BIRD Analytics? **"[BIRD analytics is a fast, high performance AI integrated tools that provides you best solutions.](https://www.g2.com/survey_responses/bird-analytics-review-9503734)"** **Rating:** 5.0/5.0 stars *— Chithra P.* [Read full review](https://www.g2.com/survey_responses/bird-analytics-review-9503734) --- **"[BIRD analytics is a one of the fast and high performance AI integrated platform.](https://www.g2.com/survey_responses/bird-analytics-review-9503443)"** **Rating:** 5.0/5.0 stars *— Kaushal K.* [Read full review](https://www.g2.com/survey_responses/bird-analytics-review-9503443) --- ### 16. [CUBO iQ® Enterprise](https://www.g2.com/products/cubo-iq-enterprise/reviews) Globalization and the emergence of new applications demand accurate correlations between entity records, which have been expressed with different schemas, formats, fields, and attributes. In a private entity, a single view of their customers is essential for Business Intelligence (BI) and more. Identity resolution is also used in applications related to data quality, such as Customer Data Management (CDM) and Master Data Management (MDM). In contexts like national security, it is possible to identify dangerous profiles through screening for patterns, providing real-time visible matches. In the case of financial services, it can identify customers associated with illicit activities such as terrorism, money laundering, and fraud (by conducting background checks). Most developed countries require compliance with Know Your Customer (KYC), Politically Exposed Person (PEP), and Office of Foreign Assets Control (OFAC) regulations. For the healthcare sector, it enables the construction of a comprehensive picture of patient-related information. The capabilities of automated identity resolution are accurate, fast, and scalable, specifically addressing these and other entity matching requirements. Vision. **Average Rating:** 4.5/5.0 **Total Reviews:** 5 **How Do G2 Users Rate CUBO iQ® Enterprise?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.3/10 (Category avg: 8.5/10) - **Data Workflow:** 6.1/10 (Category avg: 8.5/10) **Who Is the Company Behind CUBO iQ® Enterprise?** - **Seller:** [Datos Maestros™](https://www.g2.com/sellers/datos-maestros) - **Year Founded:** 2019 - **HQ Location:** Bogotá, CO - **LinkedIn® Page:** https://linkedin.com/company/datosmaestros (13 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 80% Small-Business, 20% Mid-Market #### What Are Recent G2 Reviews of CUBO iQ® Enterprise? **"[Ease of use](https://www.g2.com/survey_responses/cubo-iq-enterprise-review-9545492)"** **Rating:** 4.5/5.0 stars *— José Miguel S.* [Read full review](https://www.g2.com/survey_responses/cubo-iq-enterprise-review-9545492) --- **"[CUBO iQ an excellent tool for Data Governance](https://www.g2.com/survey_responses/cubo-iq-enterprise-review-9520160)"** **Rating:** 4.5/5.0 stars *— Fredy Yarney R.* [Read full review](https://www.g2.com/survey_responses/cubo-iq-enterprise-review-9520160) --- ### 17. [Jethro](https://www.g2.com/products/jethro/reviews) Jethro makes interactive Business Intelligence work on Big Data. (Hadoop). Jethro enables Business Intelligence users to analyze and visualize Big Data in real-time and its SQL Acceleration Engine seamlessly integrates with BI tools like Tableau or Qlik. **Average Rating:** 4.5/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Jethro?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 7.8/10 (Category avg: 8.5/10) - **Data Workflow:** 7.8/10 (Category avg: 8.5/10) **Who Is the Company Behind Jethro?** - **Seller:** [Jethro](https://www.g2.com/sellers/jethro) - **Year Founded:** 2012 - **HQ Location:** San Francisco, US - **Twitter:** @JethroData (1,999 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2894649 (45 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Enterprise, 33% Small-Business #### What Are Jethro's Pros and Cons? **Pros:** - Ease of Use (1 reviews) - Fast Querying (1 reviews) - Performance (1 reviews) - Powerful (1 reviews) - Scaling (1 reviews) **Cons:** - Expensive (2 reviews) - Complexity (1 reviews) - Difficult Setup (1 reviews) - Maintenance Issues (1 reviews) ### What Do G2 Reviewers Say About Jethro? *AI-generated summary from verified user reviews* **Pros:** - Users find Jethro's **ease of use** appealing, especially with its efficient dynamic indexing capabilities. - Users value the **fast querying** of Jethro, significantly speeding up data retrieval and enhancing efficiency. - Users benefit from Jethro's **accelerated performance** , significantly reducing query times for large data sets. - Users value the **powerful dynamic indexing** feature of Jethro, enhancing performance and efficiency during data processing. - Users value Jethro's **horizontal scaling** , enabling growth without sacrificing performance and efficiency. **Cons:** - Users find the product **expensive** to license and maintain, particularly for small organizations with limited data. - Users find the **setup complexity** of Jethro challenging, impacting their initial experience and usability. - Users find the **difficult setup** of Jethro to be quite complex, impacting their initial experience. - Users struggle with **maintenance issues** , citing high costs and hardware requirements that complicate the experience. #### What Are Recent G2 Reviews of Jethro? **"[Jethro data processing use case](https://www.g2.com/survey_responses/jethro-review-10492071)"** **Rating:** 4.0/5.0 stars *— Anand J.* [Read full review](https://www.g2.com/survey_responses/jethro-review-10492071) --- **"[very powerful software!](https://www.g2.com/survey_responses/jethro-review-10281847)"** **Rating:** 4.5/5.0 stars *— Ling G.* [Read full review](https://www.g2.com/survey_responses/jethro-review-10281847) --- #### What Are G2 Users Discussing About Jethro? - [What is Jethro used for?](https://www.g2.com/discussions/jethro-what-is-jethro-used-for) - [What is Jethro used for?](https://www.g2.com/discussions/what-is-jethro-used-for) ### 18. [Polyture](https://www.g2.com/products/polyture/reviews) Polyture combines all the major elements of the modern data stack into one application that is intuitive and free to use. The platform consists of four modules; Warehousing, Dataflows, Automated Machine Learning, and Dashboards. **Average Rating:** 5.0/5.0 **Total Reviews:** 4 **Who Is the Company Behind Polyture?** - **Seller:** [Polyture](https://www.g2.com/sellers/polyture) - **HQ Location:** Santa Clara, CA - **Twitter:** @PolytureData (25 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Recent G2 Reviews of Polyture? **"[Great for making live dashboards from Google Sheets](https://www.g2.com/survey_responses/polyture-review-5129091)"** **Rating:** 5.0/5.0 stars *— George C.* [Read full review](https://www.g2.com/survey_responses/polyture-review-5129091) --- **"[The best tool we've found for our company data](https://www.g2.com/survey_responses/polyture-review-5059135)"** **Rating:** 5.0/5.0 stars *— Kamal M.* [Read full review](https://www.g2.com/survey_responses/polyture-review-5059135) --- #### What Are G2 Users Discussing About Polyture? - [What is Polyture used for?](https://www.g2.com/discussions/what-is-polyture-used-for) ### 19. [Website Development, Web Development, Product Development](https://www.g2.com/products/website-development-web-development-product-development/reviews) About Incentius: A new age technology company that creates innovative could-enabled business intelligent solutions & out-of-the-box platforms for startups using secure & scalable technologies. We are a product engineering & data analytics services provider for next-generation enterprise growth management, enabling innovation using secure technologies and standard enterprise products. **Average Rating:** 4.7/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Website Development, Web Development, Product Development?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.2/10 (Category avg: 8.5/10) - **Data Workflow:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Website Development, Web Development, Product Development?** - **Seller:** [Incentius](https://www.g2.com/sellers/incentius) - **Year Founded:** 2013 - **HQ Location:** Pune, IN - **LinkedIn® Page:** https://www.linkedin.com/company/incentius (40 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Mid-Market #### What Are Website Development, Web Development, Product Development's Pros and Cons? **Pros:** - Easy Learning (1 reviews) - Time-Saving (1 reviews) ### What Do G2 Reviewers Say About Website Development, Web Development, Product Development? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **easy learning** aspect of the website development product, which saves time and simplifies explanations. - Users appreciate the **time-saving** aspects of website development, making explanations effortless and efficient. #### What Are Recent G2 Reviews of Website Development, Web Development, Product Development? **"[Web Development, Product Development is the best platform to develop a website for my own company](https://www.g2.com/survey_responses/website-development-web-development-product-development-review-9436447)"** **Rating:** 5.0/5.0 stars *— Parameswar C.* [Read full review](https://www.g2.com/survey_responses/website-development-web-development-product-development-review-9436447) --- **"[Best tools for web development](https://www.g2.com/survey_responses/website-development-web-development-product-development-review-10705020)"** **Rating:** 5.0/5.0 stars *— Aman K.* [Read full review](https://www.g2.com/survey_responses/website-development-web-development-product-development-review-10705020) --- ### 20. [CelerData Cloud](https://www.g2.com/products/celerdata-cloud/reviews) CelerData Cloud is the fastest, secure analytical engine that powers customer-facing and AI-driven analytics at scale, delivering consistently reliable and unbeatable performance with a future-proof architecture—ensuring real-time access to open data without ingestion delays or costly data pipelines. Powered by StarRocks, CelerData delivers 3X the performance/cost of any other solution on the market and is the only platform uniquely designed to enable users to simplify their lakehouse architecture and ditch the need for a data warehouse. CelerData is used worldwide by market-leading brands including Coinbase, Pinterest, Demandbase, and Expedia to generate critical new insights for these data-driven companies. **Average Rating:** 4.8/5.0 **Total Reviews:** 3 **How Do G2 Users Rate CelerData Cloud?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind CelerData Cloud?** - **Seller:** [CelerData](https://www.g2.com/sellers/celerdata) - **Company Website:** https://celerdata.com - **Year Founded:** 2022 - **HQ Location:** Menlo Park, US - **LinkedIn® Page:** https://www.linkedin.com/company/starrocks (65 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 67% Small-Business, 33% Enterprise #### What Are CelerData Cloud's Pros and Cons? **Pros:** - Customer Support (3 reviews) - Fast Querying (3 reviews) - Performance (3 reviews) - Fast Communication (2 reviews) - Fast Processing (2 reviews) ### What Do G2 Reviewers Say About CelerData Cloud? *AI-generated summary from verified user reviews* **Pros:** - Users value the **excellent customer support** from CelerData, enhancing their overall experience and confidence in the product. - Users value the **incredibly fast query performance** of CelerData Cloud, enhancing their data strategy significantly. - Users value the **incredibly fast query performance** of CelerData Cloud, backed by excellent support for data strategy transformation. - Users value the **fast communication** from CelerData Cloud, enhancing their data management experience with swift support. - Users value the **incredibly fast query performance** of CelerData Cloud, enhancing their data strategy confidently and effectively. #### What Are Recent G2 Reviews of CelerData Cloud? **"[Transforming Big Data with StarRocks](https://www.g2.com/survey_responses/celerdata-cloud-review-11609872)"** **Rating:** 5.0/5.0 stars *— Emre K.* [Read full review](https://www.g2.com/survey_responses/celerdata-cloud-review-11609872) --- **"[A powerful SQL engine for your most demanding workloads](https://www.g2.com/survey_responses/celerdata-cloud-review-11630143)"** **Rating:** 4.5/5.0 stars *— Ye Z.* [Read full review](https://www.g2.com/survey_responses/celerdata-cloud-review-11630143) --- ### 21. [DoubleCloud](https://www.g2.com/products/doublecloud/reviews) DoubleCloud is winding down operations. The company ceased creating new accounts on October 1, 2024, and will completely close on March 1, 2025. DoubleCloud specialized in data analytics infrastructure, offering managed services for open-source data technologies. Throughout its operations, DoubleCloud provided tools for building data pipelines, including solutions for data ingestion, storage, orchestration, ELT, and real-time visualization. The company was committed to helping businesses streamline and optimize their data operations with powerful, open-source-based technologies. **Average Rating:** 4.9/5.0 **Total Reviews:** 4 **How Do G2 Users Rate DoubleCloud?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) **Who Is the Company Behind DoubleCloud?** - **Seller:** [DoubleCloud](https://www.g2.com/sellers/doublecloud) - **Year Founded:** 2022 - **HQ Location:** Dubai, AE - **LinkedIn® Page:** https://www.linkedin.com/company/doublecloudplatform/ (6 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 75% Small-Business, 25% Mid-Market #### What Are Recent G2 Reviews of DoubleCloud? **"[Cost-effective and reliable managed service for Clickhouse](https://www.g2.com/survey_responses/doublecloud-review-9843068)"** **Rating:** 5.0/5.0 stars *— George P.* [Read full review](https://www.g2.com/survey_responses/doublecloud-review-9843068) --- **"[Best Managed Service for Kafka and Clickhouse](https://www.g2.com/survey_responses/doublecloud-review-9736764)"** **Rating:** 5.0/5.0 stars *— Adarsh T.* [Read full review](https://www.g2.com/survey_responses/doublecloud-review-9736764) --- ### 22. [Exploratory](https://www.g2.com/products/exploratory/reviews) Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms. **Average Rating:** 4.2/5.0 **Total Reviews:** 3 **How Do G2 Users Rate Exploratory?** - **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 3.3/10 (Category avg: 8.5/10) - **Data Workflow:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Exploratory?** - **Seller:** [Exploratory](https://www.g2.com/sellers/exploratory) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Exploratory's Pros and Cons? **Pros:** - ML Modeling (1 reviews) - Problem Solving (1 reviews) - Productivity Improvement (1 reviews) ### What Do G2 Reviewers Say About Exploratory? *AI-generated summary from verified user reviews* **Pros:** - Users value the **AI-assisted data organization** that simplifies analysis, making it accessible for inexperienced users. - Users benefit from the **powerful problem-solving capabilities** of Exploratory, simplifying complex data analysis with AI assistance. - Users value the **productivity improvement** from Exploratory, enabling seamless data organization and AI-assisted analysis for all skill levels. #### What Are Recent G2 Reviews of Exploratory? **"[Tech savvy and ultimate utility](https://www.g2.com/survey_responses/exploratory-review-5033661)"** **Rating:** 4.5/5.0 stars *— Shri S.* [Read full review](https://www.g2.com/survey_responses/exploratory-review-5033661) --- **"[The best data analysis app](https://www.g2.com/survey_responses/exploratory-review-11286328)"** **Rating:** 5.0/5.0 stars *— Fumiaki S.* [Read full review](https://www.g2.com/survey_responses/exploratory-review-11286328) --- #### What Are G2 Users Discussing About Exploratory? - [What does the exploratory system do?](https://www.g2.com/discussions/what-does-the-exploratory-system-do) - [What is exploratory testing software?](https://www.g2.com/discussions/what-is-exploratory-testing-software) - [Which are the main elements of exploratory testing?](https://www.g2.com/discussions/which-are-the-main-elements-of-exploratory-testing) ### 23. [Kinetica](https://www.g2.com/products/kinetica/reviews) Kinetica is the database for time & space. Kinetica makes it easy and fast to: - ingest massive amounts of IoT data and other contextual data sets - fuse data sets using spatial and temporal joins - analyze data using SQL based analytics for spatial, graph, and time-series analytics or running containerized ML models **Average Rating:** 4.3/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Kinetica?** - **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 6.7/10 (Category avg: 8.5/10) - **Data Workflow:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Kinetica?** - **Seller:** [Kinetica](https://www.g2.com/sellers/kinetica) - **Year Founded:** 2016 - **HQ Location:** Arlington, Virginia, United States - **Twitter:** @KineticaHQ (3,461 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kinetica/ (71 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Mid-Market #### What Are Recent G2 Reviews of Kinetica? **"[Beneficial especially for our wind farm activities](https://www.g2.com/survey_responses/kinetica-review-9862948)"** **Rating:** 4.5/5.0 stars *— Leslie C.* [Read full review](https://www.g2.com/survey_responses/kinetica-review-9862948) --- **"[Efficient Data analytics](https://www.g2.com/survey_responses/kinetica-review-9855352)"** **Rating:** 4.0/5.0 stars *— Surya K.* [Read full review](https://www.g2.com/survey_responses/kinetica-review-9855352) --- #### What Are G2 Users Discussing About Kinetica? - [When was Kinetica founded?](https://www.g2.com/discussions/when-was-kinetica-founded) - [How many employees does Kinetica have?](https://www.g2.com/discussions/how-many-employees-does-kinetica-have) - [Is Kinetica open source?](https://www.g2.com/discussions/is-kinetica-open-source) - [What does Kinetica do?](https://www.g2.com/discussions/what-does-kinetica-do) - 1 comment ### 24. [SAS Visual Statistics](https://www.g2.com/products/sas-visual-statistics/reviews) Multiple users can explore data, then interactively create and refine predictive models. Distributed, in-memory processing slashes model development time, quickly surfacing valuable insights you can act on. **Average Rating:** 4.4/5.0 **Total Reviews:** 24 **How Do G2 Users Rate SAS Visual Statistics?** - **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 6.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 6.7/10 (Category avg: 8.5/10) - **Data Workflow:** 6.7/10 (Category avg: 8.5/10) **Who Is the Company Behind SAS Visual Statistics?** - **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2) - **Year Founded:** 1976 - **HQ Location:** Cary, NC - **Twitter:** @SASsoftware (60,863 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1491/ (18,638 employees on LinkedIn®) - **Phone:** 1-800-727-0025 **Who Uses This Product?** - **Company Size:** 52% Enterprise, 32% Small-Business #### What Are SAS Visual Statistics's Pros and Cons? **Pros:** - Ease of Use (3 reviews) - Analytics (2 reviews) - Customer Support (2 reviews) - Machine Learning (2 reviews) - Reporting (2 reviews) **Cons:** - Expensive (3 reviews) - Learning Difficulty (2 reviews) - Cost (1 reviews) - Customization Difficulty (1 reviews) - Data Analysis Difficulty (1 reviews) ### What Do G2 Reviewers Say About SAS Visual Statistics? *AI-generated summary from verified user reviews* **Pros:** - Users find SAS Visual Statistics to be **easy to use** , requiring no training and featuring intuitive drag-and-drop capabilities. - Users value the **automated analytics** of SAS Visual Statistics for its ease of understanding and reliable insights. - Users highly value the **excellent customer support** provided by SAS Visual Statistics, enhancing their overall experience. - Users value the **fast processing and advanced analytics capabilities** of SAS Visual Statistics for effective machine learning. - Users appreciate the **reliable forecasting and reporting tools** in SAS Visual Statistics for their clarity and effectiveness. **Cons:** - Users struggle with the **high licensing cost** of SAS Visual Statistics, making it inaccessible for many potential buyers. - Users find **learning difficult** with SAS Visual Statistics due to its complexity and limitations without prior experience in SAS tools. - Users are concerned about the **high licensing cost** and substantial infrastructure requirements of SAS Visual Statistics. - Users find the **customization difficult** , affecting their ability to tailor SAS Visual Statistics to their needs. - Users find **data analysis difficulty** with SAS Visual Statistics due to limited understanding of the modeling calculations involved. #### What Are Recent G2 Reviews of SAS Visual Statistics? **"[Best tool for performing large-scale statistical and machine learning computations!!](https://www.g2.com/survey_responses/sas-visual-statistics-review-10854678)"** **Rating:** 4.0/5.0 stars *— Verified User in Banking* [Read full review](https://www.g2.com/survey_responses/sas-visual-statistics-review-10854678) --- **"[Due to information about Statistics Techniques, Handling large data, coding learning](https://www.g2.com/survey_responses/sas-visual-statistics-review-10423820)"** **Rating:** 4.5/5.0 stars *— Rahul G.* [Read full review](https://www.g2.com/survey_responses/sas-visual-statistics-review-10423820) --- ### 25. [Timbr](https://www.g2.com/products/timbr/reviews) Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity. **Average Rating:** 4.4/5.0 **Total Reviews:** 7 **How Do G2 Users Rate Timbr?** - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.5/10) - **Data Workflow:** 8.3/10 (Category avg: 8.5/10) **Who Is the Company Behind Timbr?** - **Seller:** [Timbr.ai](https://www.g2.com/sellers/timbr-ai) - **Year Founded:** 2018 - **HQ Location:** Raanana , IL - **LinkedIn® Page:** https://www.linkedin.com/company/timbr-ai (9 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 63% Small-Business, 38% Enterprise #### What Are Timbr's Pros and Cons? **Pros:** - Features (2 reviews) - SQL Support (2 reviews) - Automation (1 reviews) - Data Analysis (1 reviews) - Data Management (1 reviews) **Cons:** - Learning Curve (2 reviews) - Complex Usability (1 reviews) - Expensive (1 reviews) - Limited Customization (1 reviews) ### What Do G2 Reviewers Say About Timbr? *AI-generated summary from verified user reviews* **Pros:** - Users appreciate the **powerful data analysis features** of Timbr, enabling meaningful graphs and simplified SQL complexities. - Users value the **SQL support** in Timbr, which simplifies large dataset analysis and enhances understanding through graphs. - Users appreciate the **automation features** of Timbr, simplifying email campaigns, social media posts, and repetitive tasks. - Users value the **powerful data analysis features** of Timbr, enabling systematic insights from complex data. - Users appreciate the **meaningful data context** provided by Timbr, simplifying SQL complexities with effective graph usage. **Cons:** - Users from non-technical backgrounds find the software's **complexity challenging** , making it harder to learn and use effectively. - Users find that **complex usability** makes it challenging for non-technical individuals to fully understand Timbr. - Users find the **subscription price high** for small enterprises, limiting their ability to customize the product effectively. - Users find the **limited customization** options restrictive, especially given the high subscription cost for small businesses. #### What Are Recent G2 Reviews of Timbr? **"[Best software for data analysis and management.](https://www.g2.com/survey_responses/timbr-review-10747542)"** **Rating:** 4.5/5.0 stars *— Mohit M.* [Read full review](https://www.g2.com/survey_responses/timbr-review-10747542) --- **"["Detailed Review on Timbr: Unlocking Power of Smart AI Tool"](https://www.g2.com/survey_responses/timbr-review-11709730)"** **Rating:** 4.5/5.0 stars *— Verified User in Information Technology and Services* [Read full review](https://www.g2.com/survey_responses/timbr-review-11709730) --- ## What Is Big Data Analytics Software? [Big Data Software](https://www.g2.com/categories/big-data) ## What Software Categories Are Similar to Big Data Analytics Software? - [Analytics Platforms](https://www.g2.com/categories/analytics-platforms) - [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms) - [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) --- ## How Do You Choose the Right Big Data Analytics Software? ### What You Should Know About Big Data Analytics Software ### What is Big Data Analytics Software? The huge amount of data that is accessible to businesses today has made it a near necessity for them to implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters. With the ability to visualize and understand business data, employees can make informed decisions. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid nontechnical users. #### What Types of Big Data Analytics Software Exist? Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different user personas such as data analysts and financial analysts or providing unique services. Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets. **Self-service big data analytics tools** Self-service big data analytics tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other nondata team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to [analytics platforms](https://www.g2.com/categories/analytics-platforms), organizations use these tools to build interactive dashboards for discovering actionable insights.  **Embedded analytics solutions** Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Commonly, businesses embed analytics solutions in software such as CRMs, ERP, and portals (e.g., intranets or extranets). Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making. ** ** ### What are the Common Features of Big Data Analytics Software? Big data analytics software helps companies get a better understanding of their data. The following are some core features of this software:  **Data connectivity:** If businesses cannot connect the requisite data, then there is no use for big data analytics software. The methods for connecting data include Hadoop and [Spark integration](https://www.g2.com/categories/big-data-analytics/f/spark-integration) which allows for processing and distribution workflows on top of Apache Hadoop and Apache Spark, respectively. In addition, this software should allow for analyzing data that is stored in [data lakes](https://www.g2.com/categories/big-data-analytics/f/data-lake), data warehouses, and data lake houses. **Data transformation:** For data to be analyzed, it needs to be properly cleaned and transformed into a usable format. Big data analytics software provides features such as real-time analytics and data querying. With these features, businesses can gain a high-level view of their data in real time, allowing one to query it and better understand it. Through query languages like SQL, users can query their data and dig deeper into particular data sets and data points. **Data operations:** Once the data is connected (or integrated) and transformed, it can be analyzed. Firstly, it is important to establish data workflows, which can help in stringing together specific functions and data sets to automate analytics iterations. In addition, big data analytics software provides the ability to visualize data through dashboards, as well as [notebooks](https://www.g2.com/categories/big-data-analytics/f/notebooks) which can be used to create visualization with predefined or scheduled queries.  It is not always the case that one will access analytics via a standalone analytics platform. Therefore, some products provide [embedded analytics capabilities](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics). This allows users to access analytics inside business applications, which allows for more streamlined work since the users need not switch between applications.  Other Features of Big Data Analytics Software: [Governed Discovery](https://www.g2.com/categories/big-data-analytics/f/governed-discovery), ### What are the Benefits of Big Data Analytics Software? Data is both common and invaluable and within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data and numerous reasons to utilize big data analytics software.  Big data analytics software helps people make decisions easier by allowing teams to gain deeper insight into their data. With increased data literacy, teams across a business, from sales to marketing to finance can become more efficient and better understand how they can improve through data-driven initiatives.  With big data analytics software, businesses can ingest, integrate, and prepare big data sources. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, encourage data-driven decision making for business optimization, and discover new insights that can enhance the bottom line. **Enable data-driven decision making:** Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence (BI) tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others. **Measure and understand company performance:** Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not. **Discover new actionable insights:** Analytics tools combine data from a variety of sources, including [accounting software](https://www.g2.com/categories/accounting), [enterprise resource planning (ERP) software](https://www.g2.com/categories/erp), [CRM software](https://www.g2.com/categories/crm),[marketing automation software](https://www.g2.com/categories/marketing-automation), and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another.  Analysts can discover this impact by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Teams are generally siloed and use disparate software, so these insights that were traditionally more difficult to discover, are now made easier.  ### Who Uses Big Data Analytics Software? **Data analysts:** Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data. **Operations and supply chain teams:** A company’s supply chain frequently has many touchpoints, and as a result, many data points. Therefore, employees working in operations and supply chain teams are able to use big data analytics software to gain a better understanding of their departments and the data that is generated, such as from an ERP system. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into this software, supply chain managers can optimize a number of processes to save time and resources. **Finance teams:** Finance teams leverage big data analytics software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as [accounting software](https://www.g2.com/categories/accounting), employees such as chief financial officers (CFOs) can see how well the business is performing. As mentioned above, these employees will likely be accessing the software via self-service dashboards that were set up by data analysts. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools. **Sales and marketing teams:** Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. Through the use of both self-service analytics tools and embedded analytics solutions, they can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue. For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, analytics tools allow marketing teams to track the performance of those campaigns in one central location. **Consultants:** Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as [business intelligence (BI) consulting providers](https://www.g2.com/categories/business-intelligence-bi-consulting). These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields.  ### What are the Alternatives to Big Data Analytics Software? Alternatives to big data analytics software can replace this type of software, either partially or completely: [Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Analytics platforms might include big data integrations, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery. [Log analysis software](https://www.g2.com/categories/log-analysis): Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data. [Stream analytics software](https://www.g2.com/categories/stream-analytics) **:** When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with internet of things (IoT) data, which one frequently wants to analyze in real time. [Predictive analytics software](https://www.g2.com/categories/predictive-analytics): Broad-purpose big data analytics software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more finetuned solution.  [Text analysis software](https://www.g2.com/categories/text-analysis): Big data analytics software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data. #### Software Related to Big Data Analytics Software Related solutions that can be used together with big data analytics software include: [Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software. [Data preparation software](https://www.g2.com/categories/data-preparation) **:** A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool. ### Challenges with Big Data Analytics Software Software solutions can come with their own set of challenges.  **Need for skilled employees:** Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data.  Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house. **Data organization:** To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location.  Businesses may need to purchase a dedicated [data preparation software](https://www.g2.com/categories/data-preparation) as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis. **User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase. ### Which Companies Should Buy Big Data Analytics Software? As has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and across industries should consider some sort of analytics solution, such as big data analytics in order to make sense of that data and begin to make data-driven decisions.  **Financial services:** Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes. **Healthcare:** Within the space of healthcare, bad data practices might have dire or even deadly consequences. Big data analytics software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter. **Retail** : Retail organizations, whether they be B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, in order to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With big data analytics software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions. ### How to Buy Big Data Analytics Software #### Requirements Gathering (RFI/RFP) for Big Data Analytics Software If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more. As mentioned above, the variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data.  To find the right solution, buyers should determine pain points and jot them down. These should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy. Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce a request for information (RFI), a one-page list with a few bullet points describing what is needed from a big data analytics software. #### Compare Big Data Analytics Software Products **Create a long list** From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor. **Create a short list** From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions. **Conduct demos** To ensure the comparison is thoroughgoing, the user should demo each solution on the shortlist with the same use case and data sets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.  #### Selection of Big Data Analytics Software **Choose a selection team** As big data analytics software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities. **Negotiation** Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others. **Final decision** After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board. ### What Does Big Data Analytics Software Cost? Businesses decide to deploy big data analytics software with the goal of deriving some degree of a return on investment (ROI). #### Return on Investment (ROI) As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the big data analytics tool. ### Implementation of Big Data Analytics Software **How is Big Data Analytics Software Implemented?** Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively. **Who is Responsible for Big Data Analytics Software Implementation?** It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together their data and begin the journey of analytics, starting with proper data preparation and management. ### Big Data Analytics Software Trends **Data literacy** Business data is no longer locked up in silos. With big data analytics solutions, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) software](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data easier and more powerful, providing more accurate results. Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from all their various sources. **Shift to the cloud** The move from on-premises data analytics to the cloud has been underway for a number of years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insights. The move away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access.  **Conversational AI** Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they are looking for using intuitive language. Intuitive methods of querying data mean enabling a larger user base to access and make sense of company data. **Machine learning** AI is quickly becoming a promising feature of analytics solutions throughout the whole data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, big data analytics solutions are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and helping them to understand what they are seeing.