# Best Big Data Analytics Software *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. ## Best Big Data Analytics Software At A Glance - **Leader:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/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:** [Databricks](https://www.g2.com/products/databricks/reviews) - **Best Free Software:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) --- **Sponsored** ### Strategy Mosaic 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. [Visit company website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=paid_promo&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1041&secure%5Bmedium%5D=sponsored&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1559674&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&secure%5Btoken%5D=6a4a31bb4cd7b3392ad53c7a13ad6220bb36f8c715a6c45d080b61b366d32416&secure%5Burl%5D=https%3A%2F%2Fwww.strategysoftware.com%2Fstrategymosaic&secure%5Burl_type%5D=paid_promos) --- ## Top-Rated Products (Ranked by G2 Score) ### 1. [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. Store 10 GiB of data and run up to 1 TiB of queries for free per month. **Average Rating:** 4.5/5.0 **Total Reviews:** 1,156 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.8/10 (Category avg: 8.4/10) - **Data Workflow:** 8.7/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,840,340 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 37% Enterprise, 35% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (155 reviews) - Speed (142 reviews) - Fast Querying (119 reviews) - Integrations (117 reviews) - Query Efficiency (113 reviews) **Cons:** - Expensive (126 reviews) - Query Issues (77 reviews) - Cost Issues (62 reviews) - Cost Management (59 reviews) - Expensive Queries (53 reviews) ### 2. [Databricks](https://www.g2.com/products/databricks/reviews) Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and over 60% of the Fortune 500 — rely on Databricks to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified Data Intelligence Platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase and Unity Catalog. **Average Rating:** 4.6/5.0 **Total Reviews:** 721 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.9/10 (Category avg: 8.4/10) - **Data Workflow:** 8.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Databricks Inc.](https://www.g2.com/sellers/databricks-inc) - **Company Website:** https://databricks.com - **Year Founded:** 2013 - **HQ Location:** San Francisco, CA - **Twitter:** @databricks (89,234 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (14,779 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Senior Data Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 44% Enterprise, 40% Mid-Market #### Pros & Cons **Pros:** - Features (288 reviews) - Ease of Use (278 reviews) - Integrations (189 reviews) - Collaboration (150 reviews) - Data Management (150 reviews) **Cons:** - Learning Curve (112 reviews) - Expensive (97 reviews) - Steep Learning Curve (96 reviews) - Missing Features (69 reviews) - Complexity (64 reviews) ### 3. [Snowflake](https://www.g2.com/products/snowflake/reviews) Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW). **Average Rating:** 4.6/5.0 **Total Reviews:** 664 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.1/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.2/10 (Category avg: 8.4/10) - **Data Workflow:** 9.2/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc) - **Company Website:** https://www.snowflake.com - **Year Founded:** 2012 - **HQ Location:** San Mateo, CA - **Twitter:** @SnowflakeDB (237 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (10,857 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Data Analyst - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 44% Mid-Market, 43% Enterprise #### Pros & Cons **Pros:** - Ease of Use (87 reviews) - Scalability (67 reviews) - Data Management (66 reviews) - Features (64 reviews) - Integrations (61 reviews) **Cons:** - Expensive (52 reviews) - Cost (35 reviews) - Cost Management (32 reviews) - Learning Curve (25 reviews) - Complexity (20 reviews) ### 4. [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews) IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial **Average Rating:** 4.4/5.0 **Total Reviews:** 150 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 7.2/10 (Category avg: 8.4/10) - **Data Workflow:** 8.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Company Website:** https://www.ibm.com/us-en - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, CEO - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 34% Small-Business, 33% Enterprise #### Pros & Cons **Pros:** - Ease of Use (67 reviews) - Features (47 reviews) - Data Management (41 reviews) - Integrations (33 reviews) - Analytics (31 reviews) **Cons:** - Learning Curve (38 reviews) - Complexity (25 reviews) - Expensive (20 reviews) - Difficult Setup (17 reviews) - Difficulty (17 reviews) ### 5. [Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews) Kyvos is a semantic layer for AI and BI. It gives organizations a single, consistent, business-friendly view of their entire data estate. By standardizing how data is defined and understood, Kyvos eliminates metric drift across BI tools and ensures that LLMs and AI agents work with governed business semantics rather than raw tables. Kyvos also delivers lightning-fast analytics at massive scale and high concurrency — including granular multidimensional analysis on the cloud — without the sluggish query times and escalating cloud costs that typically come with it. Why Organizations Use Kyvos Unified Semantic Foundation for AI and BI Kyvos semantic layer standardizes how metrics, KPIs, dimensions, hierarchies, relationships, calculations, and business rules are modelled across the enterprise — so that dashboards, analytics tools, notebooks, and AI systems all operate on the same understanding of the business. Kyvos enables: - Shared semantics — one common data language across every tool, team, and system - Governed access — data exploration within defined security, role, and permission boundaries - Platform interoperability — consistent semantic context across diverse platforms and environments - AI readiness — LLMs and agents work with governed business semantics rather than raw tables or ambiguous schema AI Grounded in Business Context Kyvos grounds AI systems in the governed semantic model, ensuring they operate on established business context rather than raw schemas — improving the accuracy, traceability, and reliability of AI-generated insights. Consistent Metrics Across BI Tools Kyvos centralizes metric and KPI definitions in the semantic layer and applies them consistently across every analytics interface — eliminating metric drift and improving trust in analytics. High-Performance Analytics at Scale Kyvos delivers high-performance analytics that scale with demand, enabling: - Sub-second query performance across massive datasets - High concurrency across thousands of users and workloads - Consistent response times regardless of data volume or concurrency - No performance degradation as adoption grows - Multidimensional Analytics on the Cloud Kyvos enables deep multidimensional analytics, supporting: - Granular analysis across billions of rows - Thousands of measures and dimensions in a single model - Fast drill-down across complex hierarchies - Full analytical depth without sacrificing query speed Cloud Cost Efficiency Kyvos serves analytics through its semantic layer rather than routing every query to the warehouse — reducing compute consumption across analytics and AI workloads. As adoption grows, organizations can scale users, workloads, and analytical complexity without a corresponding rise in warehouse compute costs. **Average Rating:** 4.8/5.0 **Total Reviews:** 249 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.6/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.4/10) - **Data Workflow:** 9.6/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Kyvos Insights](https://www.g2.com/sellers/kyvos-insights) - **Company Website:** https://www.kyvosinsights.com - **Year Founded:** 2014 - **HQ Location:** Los Gatos, CA - **Twitter:** @KyvosInsights (690 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/kyvos-insights-inc-/ (150 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 55% Mid-Market, 40% Enterprise #### Pros & Cons **Pros:** - Ease of Use (125 reviews) - Speed (92 reviews) - Performance (56 reviews) - Analytics (54 reviews) - Fast Querying (50 reviews) **Cons:** - Learning Curve (35 reviews) - Difficult Setup (34 reviews) - Complexity (10 reviews) - Feature Limitations (7 reviews) - Learning Difficulty (7 reviews) ### 6. [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) Azure Databricks is a unified, open analytics platform developed collaboratively by Microsoft and Databricks. Built on the lakehouse architecture, it seamlessly integrates data engineering, data science, and machine learning within the Azure ecosystem. This platform simplifies the development and deployment of data-driven applications by providing a collaborative workspace that supports multiple programming languages, including SQL, Python, R, and Scala. By leveraging Azure Databricks, organizations can efficiently process large-scale data, perform advanced analytics, and build AI solutions, all while benefiting from the scalability and security of Azure. Key Features and Functionality: - Lakehouse Architecture: Combines the best elements of data lakes and data warehouses, enabling unified data storage and analytics. - Collaborative Notebooks: Interactive workspaces that support multiple languages, facilitating teamwork among data engineers, data scientists, and analysts. - Optimized Apache Spark Engine: Enhances performance for big data processing tasks, ensuring faster and more reliable analytics. - Delta Lake Integration: Provides ACID transactions and scalable metadata handling, improving data reliability and consistency. - Seamless Azure Integration: Offers native connectivity to Azure services like Power BI, Azure Data Lake Storage, and Azure Synapse Analytics, streamlining data workflows. - Advanced Machine Learning Support: Includes pre-configured environments for machine learning and AI development, with support for popular frameworks and libraries. Primary Value and Solutions Provided: Azure Databricks addresses the challenges of managing and analyzing vast amounts of data by offering a scalable and collaborative platform that unifies data engineering, data science, and machine learning. It simplifies complex data workflows, accelerates time-to-insight, and enables the development of AI-driven solutions. By integrating seamlessly with Azure services, it ensures secure and efficient data processing, helping organizations make data-driven decisions and innovate rapidly. **Average Rating:** 4.5/5.0 **Total Reviews:** 206 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.9/10 (Category avg: 8.4/10) - **Data Workflow:** 8.6/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,090,464 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Data Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 48% Enterprise, 26% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (9 reviews) - Features (8 reviews) - Integrations (6 reviews) - Speed (5 reviews) - Analytics (4 reviews) **Cons:** - Complexity (3 reviews) - Difficult Setup (3 reviews) - Learning Curve (3 reviews) - Slow Performance (3 reviews) - Unclear Pricing (3 reviews) ### 7. [Dataiku](https://www.g2.com/products/dataiku/reviews) Dataiku is the Platform for AI Success that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale. Built for AI success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production. Orchestration that scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation. Governance you can trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable. **Average Rating:** 4.4/5.0 **Total Reviews:** 186 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.7/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.5/10 (Category avg: 8.4/10) - **Data Workflow:** 9.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku) - **Company Website:** https://Dataiku.com - **Year Founded:** 2013 - **HQ Location:** New York, NY - **Twitter:** @dataiku (22,923 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,609 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Scientist, Data Analyst - **Top Industries:** Financial Services, Pharmaceuticals - **Company Size:** 60% Enterprise, 22% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (82 reviews) - Features (82 reviews) - Usability (46 reviews) - Easy Integrations (43 reviews) - Productivity Improvement (42 reviews) **Cons:** - Learning Curve (45 reviews) - Steep Learning Curve (26 reviews) - Slow Performance (24 reviews) - Difficult Learning (23 reviews) - Expensive (22 reviews) ### 8. [Alteryx](https://www.g2.com/products/alteryx/reviews) Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial. **Average Rating:** 4.6/5.0 **Total Reviews:** 647 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.4/10 (Category avg: 8.4/10) - **Data Workflow:** 9.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx) - **Company Website:** https://www.alteryx.com - **Year Founded:** 1997 - **HQ Location:** Irvine, CA - **Twitter:** @alteryx (26,204 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Analyst, Analyst - **Top Industries:** Financial Services, Accounting - **Company Size:** 62% Enterprise, 22% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (333 reviews) - Automation (148 reviews) - Intuitive (132 reviews) - Easy Learning (102 reviews) - Efficiency (102 reviews) **Cons:** - Expensive (88 reviews) - Learning Curve (80 reviews) - Missing Features (62 reviews) - Learning Difficulty (55 reviews) - Slow Performance (41 reviews) ### 9. [Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews) Find out what is happening in your business and take meaningful action quickly with Splunk Enterprise. Automate the collection, indexing and alerting of machine data that's critical to your operations. Uncover the actionable insights from all your data — no matter the source or format. Leverage artificial intelligence and machine learning for predictive and proactive business decisions. **Average Rating:** 4.3/5.0 **Total Reviews:** 411 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.4/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.7/10 (Category avg: 8.4/10) - **Data Workflow:** 9.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Cisco](https://www.g2.com/sellers/cisco) - **Year Founded:** 1984 - **HQ Location:** San Jose, CA - **Twitter:** @Cisco (720,884 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/cisco/ (95,742 employees on LinkedIn®) - **Ownership:** NASDAQ:CSCO **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Senior Software Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 64% Enterprise, 27% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (10 reviews) - Log Management (8 reviews) - Dashboards (6 reviews) - Data Analysis (6 reviews) - User Interface (5 reviews) **Cons:** - Expensive (8 reviews) - Learning Curve (8 reviews) - High Resource Consumption (4 reviews) - Licensing Issues (4 reviews) - Pricing Issues (4 reviews) ### 10. [Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews) Azure Synapse Analytics is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data. **Average Rating:** 4.4/5.0 **Total Reviews:** 36 **User Satisfaction Scores:** - **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:** 8.9/10 (Category avg: 8.4/10) - **Data Workflow:** 8.6/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,090,464 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Top Industries:** Information Technology and Services - **Company Size:** 43% Mid-Market, 32% Enterprise #### Pros & Cons **Pros:** - Analytics (1 reviews) - Automation (1 reviews) - Cloud Integration (1 reviews) - Cost-Effective (1 reviews) - Data Integration (1 reviews) **Cons:** - Cost Estimation (1 reviews) - Cost Management (1 reviews) - Debugging Issues (1 reviews) - Difficult Debugging (1 reviews) - Expensive (1 reviews) ### 11. [dbt](https://www.g2.com/products/dbt/reviews) dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines. **Average Rating:** 4.7/5.0 **Total Reviews:** 198 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.5/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.5/10 (Category avg: 8.4/10) - **Data Workflow:** 9.3/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran) - **Year Founded:** 2012 - **HQ Location:** Oakland, CA - **Twitter:** @fivetran (5,717 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/fivetran/ (1,738 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Analytics Engineer - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 57% Mid-Market, 27% Small-Business #### Pros & Cons **Pros:** - Ease of Use (38 reviews) - Features (22 reviews) - Automation (19 reviews) - Transformation (17 reviews) - Integrations (15 reviews) **Cons:** - Limited Functionality (14 reviews) - Dependency Issues (12 reviews) - Steep Learning Curve (10 reviews) - Error Handling (9 reviews) - Error Reporting (9 reviews) ### 12. [Starburst](https://www.g2.com/products/starburst/reviews) Starburst is the data platform for analytics, applications, and AI, unifying data across clouds and on-premises to accelerate AI innovation. Organizations—from startups to Fortune 500 enterprises in 60+ countries—rely on Starburst for fast data access, seamless collaboration, and enterprise-grade governance on an open hybrid data lakehouse. Wherever data lives, Starburst unlocks its full potential, powering data and AI from development to deployment. By future-proofing data architecture, Starburst helps businesses fuel innovation with AI. Learn more at starburst.ai **Average Rating:** 4.4/5.0 **Total Reviews:** 92 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 7.9/10 (Category avg: 8.4/10) - **Data Workflow:** 7.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Starburst](https://www.g2.com/sellers/starburst) - **Company Website:** https://www.starburst.io/ - **Year Founded:** 2017 - **HQ Location:** Boston, MA - **Twitter:** @starburstdata (3,451 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/starburstdata/ (525 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 48% Enterprise, 32% Small-Business #### Pros & Cons **Pros:** - Fast Querying (20 reviews) - Query Efficiency (18 reviews) - Integrations (17 reviews) - Ease of Use (15 reviews) - Large Datasets (14 reviews) **Cons:** - Query Issues (14 reviews) - Slow Performance (13 reviews) - Complexity (11 reviews) - Learning Curve (10 reviews) - Performance Issues (9 reviews) ### 13. [Teradata Vantage](https://www.g2.com/products/teradata-teradata-vantage/reviews) At Teradata, we believe that people thrive when empowered with better information. That’s why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers’ customers—to make better, more confident decisions. The world’s top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise. See why at Teradata.com. **Average Rating:** 4.3/5.0 **Total Reviews:** 340 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 7.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.2/10 (Category avg: 8.4/10) - **Data Workflow:** 7.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Teradata](https://www.g2.com/sellers/teradata) - **Company Website:** https://www.teradata.com - **Year Founded:** 1979 - **HQ Location:** San Diego, CA - **Twitter:** @Teradata (93,113 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1466/ (9,872 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Engineer, Software Engineer - **Top Industries:** Information Technology and Services, Financial Services - **Company Size:** 70% Enterprise, 21% Mid-Market #### Pros & Cons **Pros:** - Performance (16 reviews) - Speed (13 reviews) - Analytics (11 reviews) - Scalability (11 reviews) - Large Datasets (9 reviews) **Cons:** - Learning Curve (10 reviews) - Steep Learning Curve (5 reviews) - Complexity (4 reviews) - Not User-Friendly (4 reviews) - Poor UI Design (4 reviews) ### 14. [Confluent](https://www.g2.com/products/confluent/reviews) Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-time brand experiences. Data in motion is the underlying, fundamental ingredient to any truly connected customer experience. It provides a continuous supply of real- time event streams coupled with real-time stream processing to power the data-driven backend operations and rich front-end experiences necessary for any business to succeed within today’s competitive, consumer-driven markets. Set your data in motion while avoiding the headaches of infrastructure management and focus on what matters most: your business. Built by the original creators of Apache Kafka, Confluent Cloud is a fully managed, cloud-native service for connecting and processing all of your real-time data, everywhere it’s needed. **Average Rating:** 4.4/5.0 **Total Reviews:** 111 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.9/10 (Category avg: 8.4/10) - **Data Workflow:** 7.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Confluent](https://www.g2.com/sellers/confluent) - **Year Founded:** 2014 - **HQ Location:** Mountain View, California - **Twitter:** @ConfluentInc (43,550 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/88873/ (3,706 employees on LinkedIn®) - **Ownership:** NASDAQ: CFLT **Reviewer Demographics:** - **Who Uses This:** Senior Software Engineer, Software Engineer - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 36% Enterprise, 34% Small-Business #### Pros & Cons **Pros:** - Cloud Computing (1 reviews) - Cloud Services (1 reviews) - Connectors (1 reviews) - Data Integration (1 reviews) - Ease of Use (1 reviews) **Cons:** - Cost Estimation (1 reviews) - Expensive (1 reviews) - Initial Difficulties (1 reviews) - Lack of Features (1 reviews) - Learning Curve (1 reviews) ### 15. [Azure Data Lake Analytics](https://www.g2.com/products/azure-data-lake-analytics/reviews) Azure Data Lake Analytics is a distributed, cloud-based data processing architecture offered by Microsoft in the Azure cloud. It is based on YARN, the same as the open-source Hadoop platform. **Average Rating:** 4.2/5.0 **Total Reviews:** 28 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 7.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.1/10 (Category avg: 8.4/10) - **Data Workflow:** 8.5/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,090,464 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 54% Enterprise, 27% Mid-Market ### 16. [MATLAB](https://www.g2.com/products/matlab/reviews) MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries. **Average Rating:** 4.5/5.0 **Total Reviews:** 745 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.4/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.7/10 (Category avg: 8.4/10) - **Data Workflow:** 8.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [MathWorks](https://www.g2.com/sellers/mathworks) - **Year Founded:** 1984 - **HQ Location:** Natick, MA - **Twitter:** @MATLAB (103,430 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1194036/ (7,860 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Student, Graduate Research Assistant - **Top Industries:** Higher Education, Research - **Company Size:** 42% Enterprise, 31% Small-Business #### Pros & Cons **Pros:** - Ease of Use (19 reviews) - Features (16 reviews) - Data Visualization (13 reviews) - Tools Variety (10 reviews) - Simulation (9 reviews) **Cons:** - Expensive (12 reviews) - Slow Performance (10 reviews) - High System Requirements (7 reviews) - Expensive Licensing (4 reviews) - Lagging Performance (4 reviews) ### 17. [EXASOL](https://www.g2.com/products/exasol/reviews) Exasol is the world’s ​most powerful Analytics Engine, ​purpose-built to handle the most demanding data workloads at an unmatched price / performance ratio​. **In-memory architecture** Want to process 3 billion rows in 3 seconds, not 3 hours? Exasol manages memory cache automatically, only bringing what's needed into the database for dramatically faster access times. **Automatic query tuning** Enjoy optimized performance while minimizing data administration overhead. Exasol uses intelligent, proprietary algorithms to self-tune queries on the fly -- adding and removing indices automatically – so you can bring true self-service BI to your organization. **User defined functions (UDF)** When you need more than a SQL statement, UDF scripts allow you to program your own analysis. Take your unique machine learning and data ingest scripts written in Python, R, and Lua, and run them in our database engine. Through UDF scripts, you'll get a highly flexible interface for nearly every requirement, allowing you to bring in data quickly from wherever it lives. In addition to being the fastest, Exasol also leads in the TPC price-performance metrics, meaning everyone in your organization can take advantage of unrivaled in-memory speed at a low price. And, unlike our competitors, Exasol allows you to choose the deployment destination. Deploy in the cloud, on-premises, or hybrid to meet your organization's unique needs and preferred vendors. **Average Rating:** 4.7/5.0 **Total Reviews:** 23 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 9.2/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 9.6/10 (Category avg: 8.4/10) - **Data Workflow:** 6.7/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [EXASOL](https://www.g2.com/sellers/exasol) - **Year Founded:** 2000 - **HQ Location:** Nurnberg, Bayern - **LinkedIn® Page:** https://www.linkedin.com/company/1741694/ (215 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 39% Enterprise, 32% Mid-Market #### Pros & Cons **Pros:** - Performance (2 reviews) - Query Efficiency (2 reviews) - Analytics (1 reviews) - Cost-Effective (1 reviews) - Customer Support (1 reviews) **Cons:** - Complexity (1 reviews) - Debugging Issues (1 reviews) - Difficult Setup (1 reviews) - Limited Visualization (1 reviews) - Performance Issues (1 reviews) ### 18. [IBM Cloud Pak for Data](https://www.g2.com/products/ibm-cloud-pak-for-data/reviews) IBM Cloud Pak® for Data is a fully integrated data and AI platform that modernizes how businesses collect, organize and analyze data, forming the foundation to infuse AI across their organization. Running on Red Hat OpenShift and available on any cloud, this unified platform helps companies automate the end-to-end AI lifecycle. The intelligent data fabric in IBM Cloud Pak for Data enables automated distributed queries at scale without data movement; automated discovery and understanding of business-ready data; automated universal privacy and usage policies across the data ecosystem; and optimized model training, accuracy and explainability. View the demo: https://mediacenter.ibm.com/media/1\_je41fqqz. The platform delivers on the below use cases: • Data access and availability – Eliminate data silos and simplify your data landscape to enable faster, cost-effective extraction of value from your data. • Data quality and governance - Apply governance solutions and methodologies to deliver trusted, business data. • Data privacy and security - Fully understand and manage sensitive data with a pervasive privacy framework. • ModelOps - Automate the AI lifecycle and synchronize application and model pipelines to scale AI deployments. • AI governance – Ensure your AI is transparent, compliant and trustworthy with greater visibility into model development, with capabilities such as explainable AI, model risk management and bias detection. • AI for Financial Operations - Automate and integrate planning across your organization, from financial planning & analysis to workforce planning, sales forecasting and supply chain planning. • AI for Customer care - Reduce time to resolution, decrease call volume and increase customer satisfaction. IBM Watson Assistant (WA) can provide AI-powered automated assistance and enable human agents to better handle inquiries. IBM Watson Discovery (WD) complements Watson Assistant and can help unlock insights from complex business content. Discover IBM Cloud Pak for Data Industry Accelerators: https://dataplatform.cloud.ibm.com/gallery?context=cpdaas See a case study: https://mediacenter.ibm.com/media/1\_sr6lx8sz Try at no-cost: http://ibm.biz/dataplatformtrial **Average Rating:** 4.3/5.0 **Total Reviews:** 71 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.1/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.5/10 (Category avg: 8.4/10) - **Data Workflow:** 8.9/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (708,000 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 51% Enterprise, 28% Small-Business ### 19. [ILUM](https://www.g2.com/products/ilum-ilum/reviews) 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. **Average Rating:** 4.9/5.0 **Total Reviews:** 23 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 10.0/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 10.0/10 (Category avg: 8.4/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Ilum](https://www.g2.com/sellers/ilum) - **Company Website:** https://ilum.cloud/ - **Year Founded:** 2019 - **HQ Location:** Santa Fe, US - **Twitter:** @IlumCloud (18 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/ilum-cloud/ (4 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Telecommunications - **Company Size:** 52% Enterprise, 35% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (16 reviews) - Features (16 reviews) - Integrations (16 reviews) - Setup Ease (15 reviews) - Easy Integrations (14 reviews) **Cons:** - Complex Setup (9 reviews) - Difficult Setup (9 reviews) - Learning Curve (9 reviews) - UX Improvement (8 reviews) - Complexity (7 reviews) ### 20. [Dremio](https://www.g2.com/products/dremio/reviews) Dremio is the pioneer of The Agentic Lakehouse—the only data platform built for agents, managed by agents. Organizations need to transform ideas into actions at unprecedented speed—Dremio delivers this agility by equipping AI agents with federated data access, unstructured data processing, and rich business context through its AI Semantic Layer. In the agentic-era, data engineering teams can’t manually tune performance for thousands of users and agents asking unpredictable questions every second. Dremio’s Agentic Lakehouse autonomously manages itself, removing undifferentiated management tasks, allowing engineers to focus on initiatives that drive business results. Dremio’s agentic lakehouse automatically optimizes queries, reorganizes data, and maintains performance at any scale. Dremio is trusted by thousands of global enterprises including Shell, TD Bank, and Michelin, and built on open standards. Dremio co-created Apache Polaris and Apache Arrow, and it's the only lakehouse built natively on Apache Iceberg, Polaris, and Arrow. **Average Rating:** 4.6/5.0 **Total Reviews:** 63 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.3/10 (Category avg: 8.4/10) - **Data Workflow:** 7.1/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Dremio](https://www.g2.com/sellers/dremio) - **Year Founded:** 2015 - **HQ Location:** Santa Clara, California - **Twitter:** @dremio (5,087 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/dremio/ (362 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Financial Services, Information Technology and Services - **Company Size:** 49% Enterprise, 41% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (13 reviews) - Integrations (10 reviews) - Performance (7 reviews) - SQL Support (7 reviews) - Data Management (6 reviews) **Cons:** - Difficulty (5 reviews) - Poor Customer Support (5 reviews) - Learning Curve (4 reviews) - Difficult Setup (3 reviews) - Poor Documentation (3 reviews) ### 21. [Omniscope Evo](https://www.g2.com/products/omniscope-evo/reviews) Visokio builds Omniscope Evo, complete and extensible BI software for data processing, analytics and reporting. A smart experience on any device. Start from any data in any shape, load, blend, transform and explore it, extract insights through ML algorithms, then produce interactive reports and dashboards to share your findings. Omniscope is not only an all-in-one self-service BI tool with a responsive UX on all modern devices, but also a powerful and extensible platform: you can augment data workflows with Python / R scripts and enhance reports with any JS visualisation. Whether you’re a data manager, scientist or analyst, Omniscope is your complete solution: from data, through analytics to visualisation. 🧽 Data Prep, ETL: build workflows to load, stream, blend and transform any data. 🔍 Analytics: leverage machine learning, extract insights and perform visual exploration. 📊 Visualisation: design interactive reports, publish and share your results. 📜 Extensible: augment data pipelines with your Python / R scripts, enhance reports with any JS based visualisation. 🚀 Scalable: big data preparation and live query dashboards on SQL databases. 🤝 Collaboration: multi-user synchronised edits on workflows and dashboards. 🤖 Automation API: schedule parameterised data refresh & report updates, trigger tasks, alerts, edit & query data. 💐 Universal: a fresh and smart experience on any device: Windows, Mac, Linux, Android, iOS. 🏢 Deployment: on-premises or on your cloud. Built-in user permissions / OIDC / SSO 🎨 White-label: host branded data solutions and embedded analytics **Average Rating:** 4.7/5.0 **Total Reviews:** 21 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 9.5/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.4/10 (Category avg: 8.4/10) - **Data Workflow:** 9.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Visokio](https://www.g2.com/sellers/visokio) - **Year Founded:** 2002 - **HQ Location:** London, GB - **Twitter:** @Visokio (257 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/658108 (8 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services - **Company Size:** 65% Small-Business, 17% Enterprise ### 22. [DIAdem](https://www.g2.com/products/diadem/reviews) DIAdem is data management software for measurement data aggregation, inspection, analysis, and reporting. DIAdem is application software that helps engineers accelerate post-processing of measurement data. It is optimized for large data sets and includes tools to quickly aggregate and search for the data you need, view and investigate that data, transform it with engineering-specific analysis functions and share results with a powerful drag-and-drop report editor. You can use DIAdem with over one thousand data file formats by utilizing DataPlugins. You can leverage scripts written in Python or Visual Basic Script to automate your repetitive data post-processing tasks and transform your measurement data into complete, accurate, and actionable insights. **Average Rating:** 4.4/5.0 **Total Reviews:** 40 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 7.5/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.6/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.5/10 (Category avg: 8.4/10) - **Data Workflow:** 9.3/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [NI](https://www.g2.com/sellers/ni) - **Year Founded:** 1976 - **HQ Location:** Austin, TX - **Twitter:** @NIglobal (26,252 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3433 (8,003 employees on LinkedIn®) - **Ownership:** NASDAQ: NATI **Reviewer Demographics:** - **Top Industries:** Automotive, Mechanical or Industrial Engineering - **Company Size:** 43% Small-Business, 41% Enterprise ### 23. [Cloudera](https://www.g2.com/products/cloudera/reviews) At Cloudera, we believe data can make what is impossible today, possible tomorrow. We deliver an enterprise data cloud for any data, anywhere, from the Edge to AI. We enable people to transform vast amounts of complex data into clear and actionable insights to enhance their businesses and exceed their expectations. Cloudera is leading hospitals to better cancer cures, securing financial institutions against fraud and cyber-crime, and helping humans arrive on Mars — and beyond. Powered by the relentless innovation of the open-source community, Cloudera advances digital transformation for the world’s largest enterprises **Average Rating:** 4.0/5.0 **Total Reviews:** 34 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 7.8/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.5/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 7.0/10 (Category avg: 8.4/10) - **Data Workflow:** 8.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Cloudera](https://www.g2.com/sellers/cloudera) - **Year Founded:** 2008 - **HQ Location:** Palo Alto, CA - **Twitter:** @cloudera (106,568 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/229433/ (3,387 employees on LinkedIn®) - **Phone:** 888-789-1488 **Reviewer Demographics:** - **Top Industries:** Information Technology and Services, Computer Software - **Company Size:** 50% Enterprise, 24% Mid-Market ### 24. [Qubole](https://www.g2.com/products/qubole/reviews) Qubole is the open data lake company that provides a simple and secure data lake platform for machine learning, streaming, and ad-hoc analytics. No other platform provides the openness and data workload flexibility of Qubole while radically accelerating data lake adoption, reducing time to value, and lowering cloud data lake costs by 50 percent. Qubole’s Platform provides end-to-end data lake services such as cloud infrastructure management, data management, continuous data engineering, analytics, and machine learning with near-zero administration. Qubole is trusted by leading brands such as Expedia, Disney, Oracle, Gannett and Adobe to spur innovation and to transform their businesses for the era of big data. For more information, visit us at www.qubole.com. **Average Rating:** 4.0/5.0 **Total Reviews:** 237 **User Satisfaction Scores:** - **Has the product been a good partner in doing business?:** 8.1/10 (Category avg: 8.9/10) - **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10) - **Real-Time Analytics:** 8.0/10 (Category avg: 8.4/10) - **Data Workflow:** 7.8/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Qubole](https://www.g2.com/sellers/qubole) - **Year Founded:** 2011 - **HQ Location:** Santa Clara, CA - **Twitter:** @qubole (9,459 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2531735/ (25 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Software Engineer, Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 51% Enterprise, 44% Mid-Market ### 25. [Gigasheet](https://www.g2.com/products/gigasheet/reviews) Gigasheet is an analytics platform purpose-built for healthcare market intelligence. Gigasheet analyzes price transparency files and payer-negotiated rates at massive scale. Used by payers, consultants, and provider organizations, Gigasheet makes it easy to explore reimbursement data, benchmark pricing across payers and regions, and identify outliers using a familiar spreadsheet interface. The platform supports billions of rows and connects directly to data warehouses, cloud storage, and flat files, enabling rapid healthcare price intelligence without burdening IT resources. **Average Rating:** 4.9/5.0 **Total Reviews:** 22 **User Satisfaction Scores:** - **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:** 10.0/10 (Category avg: 8.4/10) - **Data Workflow:** 10.0/10 (Category avg: 8.5/10) **Seller Details:** - **Seller:** [Gigasheet](https://www.g2.com/sellers/gigasheet) - **Year Founded:** 2020 - **HQ Location:** Washington DC Area - **Twitter:** @gigasheet (407 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/gigasheet/ (11 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Marketing and Advertising - **Company Size:** 65% Small-Business, 17% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (6 reviews) - Usability (4 reviews) - Customer Support (3 reviews) - Large Datasets (3 reviews) - Features (2 reviews) **Cons:** - Expensive (1 reviews) ## Parent Category [Big Data Software](https://www.g2.com/categories/big-data) ## Related Categories - [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) --- ## Buyer Guide ### 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.