# Best Big Data Analytics Software

## How Many Big Data Analytics Software Products Does G2 Track?

**Total Products under this Category:** 110

### Category Stats (Jul 2026)

- **Average Rating:** 4.46/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Datacoves (+0.27%) - Among all products in this category, Datacoves recorded the largest rating increase compared to last month

_Last updated: July 27, 2026_

## How Does G2 Rank Big Data Analytics Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 8,400+ 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.

## G2 Grid® for Big Data Analytics Software
 ![G2 Grid® for Big Data Analytics Software plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-analytics/grids.png?focus%5B%5D=10470&focus%5B%5D=6073&focus%5B%5D=10938&focus%5B%5D=1308796&focus%5B%5D=67962&focus%5B%5D=989&focus%5B%5D=27024&focus%5B%5D=52199)

Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, IBM watsonx.data, Azure Databricks, Alteryx, Kyvos Semantic Layer, and Azure Synapse Analytics.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-analytics/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=azure-databricks&focus%5B%5D=alteryx&focus%5B%5D=kyvos-semantic-layer&focus%5B%5D=azure-synapse-analytics)

**Sponsored**

### Starburst

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

[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=2026-07-28T01%3A39%3A59Z&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=116699&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%252Fcubo-iq-enterprise%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fbig-data-analytics%2526source%253Dcategory&secure%5Btoken%5D=72c39f9efd2be840779da385e785750c809ac4bbed987ac00297f83cd0292cea&secure%5Burl%5D=https%3A%2F%2Fwww.starburst.io%2Ffree-trial%2F&secure%5Burl_type%5D=free_trial)

### [Databricks](https://www.g2.com/products/databricks/reviews)

Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,325

#### How Do G2 Users Rate Databricks?

- **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: 8.5/10)
- **Data Workflow:** 8.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Databricks?

- **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 (92,269 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3477522/ (15,627 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 48% Large, 38% Medium

#### What Do G2 Reviewers Say About Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications.
- Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services.
- Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency.
- Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights.
- Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization.

##### Cons

- Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability.
- Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects.
- Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools.
- Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes.
- Users face **complex setup** challenges initially, though support helps simplify the experience over time.

#### What Are Recent G2 Reviews of Databricks?

**["Helpful for Managing and Analyzing Operational Data"](https://www.g2.com/survey_responses/databricks-review-13090803)**

**Rating:** 4.5/5.0 stars

_— Vishaka C._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13090803)

**["Helpful for Rider Service Operations Reporting"](https://www.g2.com/survey_responses/databricks-review-13130892)**

**Rating:** 4.0/5.0 stars

_— Jayesh W._

[Read full review](https://www.g2.com/survey_responses/databricks-review-13130892)

### [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)

BigQuery is an AI-ready, petabyte-scale, and cost-effective data warehouse that lets you run analytics over vast amounts of data in near real time. 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:** 1,144

#### How Do G2 Users Rate Google Cloud BigQuery?

- **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: 8.5/10)
- **Data Workflow:** 8.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Google Cloud BigQuery?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,899,995 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (341,888 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Large, 35% Medium

#### What Do G2 Reviewers Say About Google Cloud BigQuery?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Google Cloud BigQuery, enabling quick analysis of massive datasets without hassle.
- Users appreciate the **exceptional speed** of BigQuery, allowing for quick processing of large datasets seamlessly.
- Users love the **easy integration** with Google Cloud services, enabling smooth data analysis and management.
- Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, allowing effortless analysis of massive datasets.
- Users appreciate the **query efficiency** of BigQuery, effortlessly processing complex queries on massive datasets with speed.

##### Cons

- Users find the **costs can escalate quickly** with Google Cloud BigQuery, requiring careful query optimization to manage expenses.
- Users struggle with **query issues** in BigQuery, facing rising costs and challenges in query optimization and troubleshooting.
- Users find **cost management challenging** with Google Cloud BigQuery due to unpredictable pricing and incidents of unexpected charges.
- Users experience **cost issues** with Google Cloud BigQuery, struggling with unexpected high bills and limited pricing visibility.
- Users find the **steep learning curve** of Google Cloud BigQuery challenging, particularly for advanced features and optimization techniques.

#### What Are Recent G2 Reviews of Google Cloud BigQuery?

**["Easy-to-Use Cloud Tool with Shareable, Saved Queries"](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)**

**Rating:** 4.0/5.0 stars

_— Reetika P._

[Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)

**["Scalable, Secure BigQuery That Connects Seamlessly Across Services"](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747)**

**Rating:** 5.0/5.0 stars

_— Aayush M._

[Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747)

### [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:** 4.5/5.0

**Total Reviews:** 711

#### How Do G2 Users Rate Snowflake?

- **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: 8.5/10)
- **Data Workflow:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

- **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc)
- **Company Website:** https://www.snowflake.com
- **Year Founded:** 2012
- **HQ Location:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB (278 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (11,308 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 45% Medium, 43% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Snowflake, finding it fast and effective for data sharing and analytics.
- Users value the **reliable features** of Snowflake, enjoying its intuitive interface and seamless data integration for analytics.
- Users find Snowflake's **data management capabilities** excellent for efficiently aggregating and querying across multiple datasets.
- Users admire the **seamless scalability** of Snowflake, effortlessly accommodating work demands and ensuring optimal performance.
- Users appreciate the **fast data analysis** of Snowflake, enabling quick insights without infrastructure worries.

##### Cons

- Users find Snowflake's **high costs** burdensome, especially for small businesses with limited budgets.
- Users find **feature limitations** in Snowflake, such as lack of code blocks and challenge in permissions management.
- Users find that **cost management** requires discipline, as unexpected charges can accumulate quickly without careful monitoring.
- Users find the **cost structure difficult to optimize** , leading to unexpectedly high initial expenses during implementation.
- Users find Snowflake's **limited features** in dynamic scripts and monitoring hinder flexibility and usability.

#### What Are Recent G2 Reviews of Snowflake?

**["Elastic Scaling and Fast Analytics with Snowflake"](https://www.g2.com/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/survey_responses/snowflake-review-13129003)

**["Snowflake Simplifies Data Management at Scale"](https://www.g2.com/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/survey_responses/snowflake-review-12898129)

### [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:** 162

#### How Do G2 Users Rate IBM watsonx.data?

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 8.5/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 7.3/10 (Category avg: 8.5/10)
- **Data Workflow:** 8.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM watsonx.data?

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** https://www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity (74,660 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (328,202 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, CEO
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 34% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.data?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of IBM watsonx.data, finding it reliable and efficient for data management tasks.
- Users value the **organized data integration** and intuitive interface of IBM watsonx.data, enhancing efficiency and analytics.
- Users value the **organized and efficient data management** of IBM watsonx.data, enhancing analytics and reporting tasks seamlessly.
- Users value the **seamless data source integration** in IBM watsonx.data, enhancing flexibility and efficiency for diverse projects.
- Users value the **ability to unify data across hybrid environments** , enhancing flexibility and driving informed decision-making.

##### Cons

- Users find the **learning curve steep** , making initial setup and navigation challenging for newcomers to IBM watsonx.data.
- Users find the **complexity** of setting up IBM watsonx.data a barrier, especially for newcomers to IBM technologies.
- Users find the **pricing steep** for IBM watsonx.data, making it less accessible for smaller businesses and projects.
- Users find the **difficult setup** of IBM watsonx.data time-consuming, with a steep learning curve and complex configurations.
- Users find IBM watsonx.data **difficult to navigate** , especially for beginners and those unfamiliar with AI and data analytics.

#### What Are Recent G2 Reviews of IBM watsonx.data?

**["Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)

**["Unified Data Management with Learning Curve"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)**

**Rating:** 5.0/5.0 stars

_— Anchal P._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12817742)

### [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:** 212

#### How Do G2 Users Rate Azure Databricks?

- **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: 8.5/10)
- **Data Workflow:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Databricks?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 47% Large, 27% Medium

#### What Do G2 Reviewers Say About Azure Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Azure Databricks to be **very easy to use and implement** , enhancing their development experience significantly.
- Users value the **rich feature set** of Azure Databricks, highlighting its excellent integration and support for multiple languages.
- Users value the **seamless integrations** of Azure Databricks with Azure services, enhancing efficiency and simplifying workflows.
- Users benefit from the **impressive speed** of Azure Databricks for large-scale data processing and implementation.
- Users value the **efficiency of analytics** in Azure Databricks, enabling streamlined data processing and insights generation.

##### Cons

- Users find Azure Databricks' **complexity** challenging, particularly during initial setup and cluster management.
- Users find the **difficult setup** of Azure Databricks challenging, making initial configuration a complex process.
- Users experience a **steep learning curve** with Azure Databricks, finding it challenging while adapting to its complexity.
- Users experience **slow performance** with Azure Databricks, particularly during cluster startup and parallel processing tasks.
- Users often find **unclear pricing** for Azure Databricks, as costs can escalate quickly without proper monitoring.

#### What Are Recent G2 Reviews of Azure Databricks?

**["A powerhouse for scaling ML workflows, but keep a close eye on your billing."](https://www.g2.com/survey_responses/azure-databricks-review-12976834)**

**Rating:** 5.0/5.0 stars

_— Lokesh S._

[Read full review](https://www.g2.com/survey_responses/azure-databricks-review-12976834)

**["Azure Databricks efficient for large data, a bit rough on edges"](https://www.g2.com/survey_responses/azure-databricks-review-12684122)**

**Rating:** 4.5/5.0 stars

_— Wealth A._

[Read full review](https://www.g2.com/survey_responses/azure-databricks-review-12684122)

### [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:** 851

#### How Do G2 Users Rate Alteryx?

- **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:** 8.4/10 (Category avg: 8.5/10)
- **Data Workflow:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,149 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,304 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Large, 21% Medium

#### What Do G2 Reviewers Say About Alteryx?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis.
- Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient.
- Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience.
- Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding.

##### Cons

- Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups.
- Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly.
- Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility.
- Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners.
- Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx.

#### What Are Recent G2 Reviews of Alteryx?

**["Powerful, Time-Saving Data Prep But Pricey, Windows-Only, and Weak on Reporting"](https://www.g2.com/survey_responses/alteryx-review-12999843)**

**Rating:** 4.5/5.0 stars

_— jayesh l._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12999843)

**["Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"](https://www.g2.com/survey_responses/alteryx-review-12983224)**

**Rating:** 4.5/5.0 stars

_— Akhil S._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224)

### [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:** 266

#### How Do G2 Users Rate Kyvos Semantic Layer?

- **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: 8.5/10)
- **Data Workflow:** 9.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Kyvos Semantic Layer?

- **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 (689 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kyvos-insights-inc-/ (152 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Senior Software Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 57% Medium, 38% Large

#### What Do G2 Reviewers Say About Kyvos Semantic Layer?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Kyvos, enabling quick insights and a user-friendly experience for complex data.
- Users love the **speed** of Kyvos for real-time insights, enabling quick queries and faster decision-making across data metrics.
- Users value the **exceptional performance** of Kyvos for quickly analyzing large datasets and delivering timely insights.
- Users admire the **lightning-fast analytics** of Kyvos Semantic Layer, enhancing performance and visualization of large datasets.
- Users value the **fast querying capabilities** of Kyvos Semantic Layer, enabling quick analysis of large transaction datasets.

##### Cons

- Users find the **learning curve steep** for advanced features and MDX queries, which can slow down usage efforts.
- Users find the **difficult setup** of Kyvos Semantic Layer challenging, though support helps ease the process.
- Users find the **initial setup and MDX complexity** challenging, though support helps ease the deployment process.
- Users note the **feature limitations** of Kyvos, especially lacking advanced analytics and integration for seamless data exploration.
- Users experience **connectivity issues** , as initial integration with existing systems can be time-consuming.

#### What Are Recent G2 Reviews of Kyvos Semantic Layer?

**["Fast, Consistent Data Exploration Across Dimensions with Kyvos Semantic Layer"](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12911098)**

**Rating:** 5.0/5.0 stars

_— ashish r._

[Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-12911098)

**["Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data"](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-13142366)**

**Rating:** 5.0/5.0 stars

_— Nikhil K._

[Read full review](https://www.g2.com/survey_responses/kyvos-semantic-layer-review-13142366)

### [Dataiku](https://www.g2.com/products/dataiku/reviews)

Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 8.8/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.6/10 (Category avg: 8.5/10)
- **Data Workflow:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Dataiku?

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** https://Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku (22,917 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataiku/ (1,619 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Scientist, Data Analyst
- **Top Industries:** Financial Services, Pharmaceuticals
- **Company Size:** 60% Large, 22% Medium

#### What Do G2 Reviewers Say About Dataiku?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate how Dataiku facilitates **easy ML development** , allowing focus on building models without the complexity.
- Users love the **ease of use** in Dataiku, simplifying complex tasks and enhancing their data analysis experience.
- Users appreciate the **ease of usability** in Dataiku, enabling collaboration for both technical and non-technical users.
- Users appreciate the **easy integrations** of Dataiku, facilitating smooth collaboration and deployment across various analytics tools.
- Users benefit from the **productivity improvement** of Dataiku, enabling faster project development and enhanced career growth.

##### Cons

- Users find the **steep learning curve** of Dataiku challenging, making it tough for beginners to master the platform.
- Users find the **steep learning curve** challenging for beginners, impacting their ability to effectively use Dataiku.
- Users find the **difficult learning** curve challenging, particularly for beginners navigating advanced features.
- Users experience **slow performance** with Dataiku when handling large datasets, affecting efficiency and productivity.
- Users find Dataiku **expensive** , especially for smaller organizations and projects, impacting accessibility and affordability.

#### What Are Recent G2 Reviews of Dataiku?

**["Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"](https://www.g2.com/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13125252)

**["Build Faster Workflows with Connected Data from many providers or distinct data sources"](https://www.g2.com/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13120436)

### [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:** 415

#### How Do G2 Users Rate Splunk Enterprise?

- **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: 8.5/10)
- **Data Workflow:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Splunk Enterprise?

- **Seller:** [Cisco](https://www.g2.com/sellers/cisco)
- **Year Founded:** 1984
- **HQ Location:** San Jose, CA
- **Twitter:** @Cisco (720,366 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cisco/ (95,545 employees on LinkedIn®)
- **Ownership:** NASDAQ:CSCO

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Senior Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 64% Large, 27% Medium

#### What Do G2 Reviewers Say About Splunk Enterprise?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **innovation** of Splunk Enterprise, appreciating its user-friendly features and powerful analytics capabilities.
- Users value the **customization features** of Splunk Enterprise, enabling tailored insights and dynamic dashboards for effective monitoring.
- Users commend the **ease of use** of Splunk Enterprise, enhancing effective monitoring and quick issue resolution.
- Users value the **efficient log management** capabilities of Splunk Enterprise for accurate analysis and insights.
- Users value the **powerful reporting features** of Splunk Enterprise, enhancing data analysis and visualization capabilities significantly.

##### Cons

- Users find Splunk Enterprise to be **expensive** , especially as data volumes grow, impacting smaller teams' operations.
- Users face a **steep learning curve** with Splunk Enterprise, which can hinder quick mastery of its features.
- Users highlight the **expensive licensing** of Splunk Enterprise, making it difficult for some companies to adopt.
- Users face **integration issues** with Splunk Enterprise, requiring better add-ons and simpler architecture for easier deployment.
- Users feel that Splunk Enterprise has **missing features** , lacking important add-ons and more flexible data onboarding options.

#### What Are Recent G2 Reviews of Splunk Enterprise?

**["SPL search and dashboards are really useful"](https://www.g2.com/survey_responses/splunk-enterprise-review-12547655)**

**Rating:** 4.0/5.0 stars

_— Nishith J._

[Read full review](https://www.g2.com/survey_responses/splunk-enterprise-review-12547655)

**["Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure"](https://www.g2.com/survey_responses/splunk-enterprise-review-12045230)**

**Rating:** 4.5/5.0 stars

_— RaviShankar S._

[Read full review](https://www.g2.com/survey_responses/splunk-enterprise-review-12045230)

### [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:** 208

#### How Do G2 Users Rate dbt?

- **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: 8.5/10)
- **Data Workflow:** 9.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind dbt?

- **Seller:** [dbt Labs](https://www.g2.com/sellers/dbt-labs)
- **Year Founded:** 2016
- **HQ Location:** Philadelphia, US
- **Twitter:** @getdbt (14,792 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dbtlabs/ (874 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Analytics Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 56% Medium, 27% Small

#### What Do G2 Reviewers Say About dbt?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of dbt, thanks to its clear structure, intuitive documentation, and seamless integration.
- Users value dbt for its **integration of software engineering best practices** , enhancing maintainability and collaboration in SQL transformations.
- Users value the **automation** features of dbt, significantly enhancing SQL code maintainability and transforming data workflows.
- Users value the **transformative power** of dbt, efficiently organizing and modeling data for actionable insights.
- Users value dbt for its **high data quality** , ensuring integrity and enhancing analytics workflows through modularization and documentation.

##### Cons

- Users face challenges with **limited functionality** in dbt due to rigid models and debugging difficulties, affecting project progress.
- Users often face **dependency issues** with dbt, leading to time-consuming troubleshooting and disruption in workflows.
- Users find the **steep learning curve** of mastering concepts like Jinja and Git to be quite challenging.
- Users struggle with **unhelpful error messages** in dbt, making troubleshooting difficult and frustrating.
- Users face **confusing error reporting** that complicates troubleshooting and hinders quick identification of issues.

#### What Are Recent G2 Reviews of dbt?

**["dbt Streamlines Data Pipelines with Powerful Incremental and SCD2 Features"](https://www.g2.com/survey_responses/dbt-review-12712114)**

**Rating:** 5.0/5.0 stars

_— Hithesh P._

[Read full review](https://www.g2.com/survey_responses/dbt-review-12712114)

**["Simple SQL-Driven Materializations with Powerful Lineage"](https://www.g2.com/survey_responses/dbt-review-12985641)**

**Rating:** 5.0/5.0 stars

_— Anish G._

[Read full review](https://www.g2.com/survey_responses/dbt-review-12985641)

### [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews)

Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI. Learn more at Teradata.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 356

#### How Do G2 Users Rate Teradata Autonomous Knowledge Platform?

- **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: 8.5/10)
- **Data Workflow:** 7.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Teradata Autonomous Knowledge Platform?

- **Seller:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/sellers/teradata-autonomous-knowledge-platform)
- **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/ (9,901 employees on LinkedIn®)

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 69% Large, 22% Medium

#### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users highlight the **extreme performance** of the Teradata Autonomous Knowledge Platform, especially for processing large data volumes efficiently.
- Users value the **high performance query execution** in Teradata, enhancing their business analytics capabilities significantly.
- Users value the **scalability** of Teradata Autonomous Knowledge Platform, enhancing data integration and operational efficiency significantly.
- Users commend the **high performance and speed** of Teradata, efficiently processing large datasets without issues.
- Users value the **fast processing of large datasets** with Teradata, praising its performance and stability during operations.

##### Cons

- Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, impacting adoption and productivity temporarily.
- Users find the **steep learning curve** of Teradata Autonomous Knowledge Platform challenging, particularly for those lacking technical expertise.
- Users find the **complexity** of Teradata's platform challenging, particularly for non-technical users and new adopters.
- Users express concerns over the **cost management requirements** needed to avoid potential misusage and performance issues.
- Users feel the **high cost** of Teradata Autonomous Knowledge Platform is a significant drawback affecting accessibility.

#### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform?

**["Teradata Vantage Fast Query Performance and Strong Analytics for Big Data"](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)**

**Rating:** 5.0/5.0 stars

_— Muzammil M._

[Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)

**["Teradata Vantage Excels at Big Data Processing and Advanced Analytics"](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)**

**Rating:** 4.5/5.0 stars

_— Nijat I._

[Read full review](https://www.g2.com/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)

### [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:** 99

#### How Do G2 Users Rate Starburst?

- **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:** 8.0/10 (Category avg: 8.5/10)
- **Data Workflow:** 7.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Starburst?

- **Seller:** [Starburst](https://www.g2.com/sellers/starburst)
- **Company Website:** https://www.starburst.io/
- **Year Founded:** 2017
- **HQ Location:** Boston, MA
- **Twitter:** @starburstdata (3,454 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/starburstdata/ (539 employees on LinkedIn®)

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 46% Large, 32% Small

#### What Do G2 Reviewers Say About Starburst?

_AI-generated summary from verified user reviews_

##### Pros

- Users highlight the **fast querying** capabilities of Starburst, enabling quick data access and analysis across various sources.
- Users appreciate the **query efficiency** of Starburst, allowing quick access to diverse data sources effortlessly.
- Users value the **seamless integration** with various data sources, significantly enhancing data access and analysis efficiency.
- Users appreciate the **ease of use** of Starburst, enabling efficient data access and real-time analysis from various sources.
- Users commend Starburst for its **superior performance** , rapidly retrieving accurate data and enhancing overall productivity.

##### Cons

- Users often experience **query issues** with Starburst, finding it less efficient for complex queries compared to traditional SQL editors.
- Users find the **initial setup complexity** of Starburst challenging, especially with configuration and optimizing queries.
- Users note a **steep learning curve** when setting up Starburst, making onboarding and optimization challenging for new users.
- Users often experience **slow performance** on Starburst, especially during peak usage and with complex queries.
- Users report **performance issues** with Starburst, especially when executing complex queries, causing significant slowdowns.

#### What Are Recent G2 Reviews of Starburst?

**["Starburst Enterprise Review"](https://www.g2.com/survey_responses/starburst-review-10604384)**

**Rating:** 4.0/5.0 stars

_— Rajiv B._

[Read full review](https://www.g2.com/survey_responses/starburst-review-10604384)

**["Transforming Enterprise Analytics and Enabling AI Through Data Federation"](https://www.g2.com/survey_responses/starburst-review-11962416)**

**Rating:** 4.0/5.0 stars

_— Verified User in Financial Services_

[Read full review](https://www.g2.com/survey_responses/starburst-review-11962416)

### [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

#### How Do G2 Users Rate Azure Data Lake Analytics?

- **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: 8.5/10)
- **Data Workflow:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Data Lake Analytics?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,091,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (231,632 employees on LinkedIn®)
- **Ownership:** MSFT

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 54% Large, 27% Medium

#### What Are Recent G2 Reviews of Azure Data Lake Analytics?

**["The power house of data management"](https://www.g2.com/survey_responses/azure-data-lake-analytics-review-7674920)**

**Rating:** 5.0/5.0 stars

_— Sam L._

[Read full review](https://www.g2.com/survey_responses/azure-data-lake-analytics-review-7674920)

**["Great service to manage your big data needs"](https://www.g2.com/survey_responses/azure-data-lake-analytics-review-5253914)**

**Rating:** 4.5/5.0 stars

_— Abhishek C._

[Read full review](https://www.g2.com/survey_responses/azure-data-lake-analytics-review-5253914)

- ‹ Prev‹ Prev
- 1
- [2](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=2#product-list)
- [3](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=3#product-list)
- [4](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=4#product-list)
- [5](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=5#product-list)
- …
- [7](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=7#product-list)
- [8](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=8#product-list)
- [Next ›Next ›](/categories/big-data-analytics?open_modal_url=%2Fproducts%2Fcubo-iq-enterprise%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-analytics%26source%3Dcategory&order=g2_score&page=2#product-list)

Spotlight Categories

[VoIP Providers](https://www.g2.com/categories/voip)

[Security Compliance Software](https://www.g2.com/categories/security-compliance)

[Fleet Management Software](https://www.g2.com/categories/fleet-management)

[Travel Management Solutions](https://www.g2.com/categories/travel-management)

[Workforce Management Software](https://www.g2.com/categories/workforce-management)

Similar Categories

- [Big Data Processing and Distribution](/categories/big-data-processing-and-distribution)

- [Event Stream Processing](/categories/event-stream-processing)

[Browse Big Data Analytics Themes](/categories/big-data-analytics/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

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 Tools at a Glance

| 

 | 

Unified lakehouse ETL, analytics, and ML pipelines

 | 

User Review

"Helpful for Managing and Analyzing Operational Data"

 |
| 

 | 

Serverless SQL analytics on petabyte-scale datasets

 | 

User Review

"Easy-to-Use Cloud Tool with Shareable, Saved Queries"

 |
| 

 | 

Elastic multi-workload analytics with zero-infrastructure overhead

 | 

User Review

"Elastic Scaling and Fast Analytics with Snowflake"

 |
| 

 | 

Federated lakehouse querying across hybrid data environments

 | 

User Review

"Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"

 |
| 

 | 

Unified Spark-native lakehouse ETL and ML

 | 

User Review

"A powerhouse for scaling ML workflows, but keep a close eye on your billing."

 |
| 

 | 

No-code ETL and multi-source data blending

 | 

User Review

"Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"

 |
| 

 | 

Sub-second OLAP querying on cloud-scale datasets

 | 

User Review

"Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data"

 |
| 

 | 

Unified ETL and big data warehousing on Azure

 | 

User Review

"Unified Data Warehousing and Big Data in One Powerful Platform"

 |
| 

 | 

End-to-end ML pipelines with low-code collaboration

 | 

User Review

"Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"

 |
| 

 | 

Cross-source log correlation and security analytics

 | 

User Review

"Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure"

 |

* * *

Show More

* * *

## 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.