Top data intelligence software for your business

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Data intelligence solutions are becoming increasingly important as businesses strive to make the most of their data. Big data is only getting bigger, and it can be challenging to process and make sense of all that information without the help of specialized software. That’s where data intelligence solutions come in. They automate the process of big data processing and analysis, making it easier for businesses to get a clear picture of what’s going on within their data sets. Data intelligence tools also go by various other names, such as big data analytics, business intelligence, and data analysis software.

These tools rely on artificial intelligence and machine learning to get valuable business insights quickly. Decision makers leverage the power of these tools to make predictions and recommendations.

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Below are some of the best data intelligence software solutions available today for big data processing and automation.

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Top data intelligence solutions

Snowflake

Snowflake logo.
Image: Snowflake

Snowflake is at the forefront of big data innovation. The platform offers a unique and powerful solution for data warehousing, data lakes, data engineering and data science. With Snowflake, organizations can discover and share governed data while executing diverse analytic workloads with near-unlimited scale and concurrency.

The Data Cloud is a global network where thousands of organizations can mobilize data with Snowflake’s platform. This makes it easy for users to access the data they need when they need it from wherever they are.

Key features

  • Snowgrid: Snowflake’s Snowgrid feature provides a governed data platform that makes it possible to share data between teams, business units and partners. The platform has cross-cloud governance controls and flexible policies that ensure data security and continuity.
  • Intelligent infrastructure: Snowflake is designed for high availability and dependability. It offers multi-cluster compute resources and easy administration.
  • Elastic performance engine: Maximizes performance by automatically adapting to changing workloads, including data pipelines and big data analytical queries.
  • Optimized storage: By centralizing all types of data, Snowflake ensures that there is no siloing and provides access to the data regardless of its structure.

Pros

  • Works on multiple tabs and queries
  • Auto Pause and auto suspend modes
  • Support for different databases when users need to query the data point
  • Separate canvas displays the status, query progress time and SQL tree structure
  • Decoupled compute and storage ensures that you only pay for what you use
  • Near zero admin tasks as the vendor takes care of most issues
  • Good customer support

Cons

Pricing

Pricing is based on the data volume and compute time with a free 30-day trial when you initially sign up for the service.

Databricks Lakehouse Platform

Databricks logo.
Image: Databricks

Databricks was founded by the team that created Apache Spark. The platform provides a unified workspace that lets users access all of their data regardless of where it’s stored. It also offers an intelligent infrastructure that optimizes compute resources and job execution.

Key features

  • Automated data transformation and processing: The Databricks platform automatically transforms and processes data so users can focus on their analysis.
  • Streamlined data ingestion: The platform offers a simplified way to ingest data, so users can quickly get started with their analysis.
  • Data pipeline monitoring: The Databricks platform provides users with the ability to monitor their data pipelines, such as alerts, jobs and runs.
  • Efficiently orchestrate pipelines: The platform helps users orchestrate their data pipelines with its visual interface and drag-and-drop functionality.
  • Advanced features and integrations: Databricks offers advanced features, such as machine learning, streaming and SQL analytics. It also integrates with popular data platforms, such as Amazon SNS and Apache Kafka.

Pros

  • Unified workspace makes it easy to work with big data sets
  • Intelligent infrastructure optimizes compute resources and job execution
  • Highly flexible because it is built on open-source standards
  • Advanced features and integrations offer a comprehensive solution for data teams
  • Good customer support

Cons

  • Complex pricing structure

Pricing

Databricks Lakehouse Platform’s price is based on your compute usage, cloud service provider and geographical location. If you use your cloud instead of opting for one of Databricks’, you receive a 14-day free trial.

Microsoft SQL Server

Microsoft logo.
Image: Microsoft

Microsoft SQL Server is a relational database management system that supports various data types. You can use the SQL Server on Windows, Linux and Docker containers to build intelligent applications in your preferred language and environment. With its comprehensive features, Microsoft SQL Server is a good choice for data-intensive applications.

Key features

  • High performance and scalability: Microsoft SQL Server is designed for high performance and can scale to meet the demands of data-intensive applications.
  • Wide adoption: It is widely used, so a large community of users and developers can offer support.
  • Security and availability: The platform offers security and availability features, such as encryption and failover clustering.
  • Connect to cloud Azure SQL servers: You can create scalable cloud services on Azure using Microsoft SQL Server, and you’ll get a single SQL platform with built-in security.
  • Integration: Microsoft SQL Server integrates with other software products, such as SharePoint and Exchange.
  • Easy data restoration and recovery: The platform provides a way to restore and recover lost or corrupted data using several methods, such as backups, log files and caching.

Pros

  • Production-ready and extensive support
  • Low maintenance SQL server
  • Built-in AI
  • Mobile BI

Cons

  • Upgrades can sometimes result in long downtimes

Pricing

The tool has a complex pricing structure with three paid editions: Enterprise, Standard and Express. The Enterprise edition is the most expensive. The exact cost is based on the volume and hosting requirements.

There is also a web version where clients only pay for hosting. In addition, there are two free versions of Microsoft SQL Server: Developer and Express.

Google Cloud BigQuery

Google Cloud BigQuery logo.
Image: Google

BigQuery sits at the heart of Google’s Data Cloud, allowing users to integrate data, scale analytics operations, share data experiences complete with inbuilt BI features, and train and deploy ML models using only SQL.

Key features

  • Serverless: BigQuery is a serverless platform, so you don’t need to provision or manage any infrastructure.
  • Multicloud data analysis with BigQuery Omni: BigQuery Omni is an analytics solution that helps you analyze data from multiple clouds, such as AWS and Azure.
  • ML and predictive modeling with BigQuery ML: BigQuery ML allows data scientists and data analysts to construct and operationalize machine learning models directly on Google’s BigQuery, using SQL and utilizing massive datasets.
  • Interactive data analysis with BigQuery BI Engine: The BigQuery BI Engine is a real-time analysis service built into BigQuery that allows users to interactively analyze data sets with sub-second query response time and high concurrency.
  • Geospatial analysis with BigQuery GIS: BigQuery GIS provides serverless functionality to BigQuery and native support for geospatial analysis, allowing you to combine your analytics processes with location intelligence.

Pros

  • No infrastructure to manage
  • Can handle large amounts of data
  • Query format is very similar to that of MySQL
  • More straightforward pricing model compared with other tools on this list
  • Integrates well with other Google products

Cons

  • Difficult to share data, tables or subset of the dataset with customers

Pricing

There are two main pricing plans: Analysis and Storage. The Analysis plan is charged per terabyte processed, and storage is charged per gigabyte per month.

Dremio

Dremio logo.
Image: Dremio

Dremio offers an open lakehouse built on open community standards such as Apache Arrow and Apache Iceberg. It helps you query, analyze and transform data without needing a central data warehouse.

Dremio provides users with self-service data and users can say goodbye to complex ETL jobs and cubes.

Key features

  • Self-service data preparation: Dremio allows users to prepare their data for analysis without relying on IT or data engineering resources.
  • Data lake acceleration: The platform accelerates data lake performance by orders of magnitude so that users can get the answers they need in seconds instead of hours or days.
  • Real-time analytics: Dremio provides real-time analytics so that users can get the most up-to-date insights into their data.
  • Unlimited scalability: The solution is highly scalable and can handle any size data set, from a few gigabytes to a petabyte.

Pros

  • Open lakehouse built on community standards
  • Fast SQL query service
  • Can connect to any data source
  • Suitable for self-service data preparation

Cons

  • Company is relatively new, founded in 2015.

Pricing

Dremio has two platform options: Dremio Cloud, a cloud-hosted option, and Dremio Software, an on-prem option. Dremio Cloud has a free Standard Tier and an enterprise tier whose pricing starts at $0.39 per Dremio Consumption Unit. Dremio Software comes in two versions, Community and Enterprise, whose pricing is provided upon request.

Vertica

Vertica logo.
Image: Vertica

Vertica provides a unified analytics platform with a broad set of analytical functions, including event and time series analysis, pattern matching, geospatial data analysis and machine learning. This platform enables data analysts to apply these functions to workloads, providing them with predictive business insights.

Key features

  • Vertica-as-a-Service or Vertica Unified Analytics Platform: Deploy Vertica-as-a-Service, or manage it yourself on any cloud — public, private or hybrid.
  • Scale and performance: With Vertica, you can scale up to several nodes and process up to 72 terabytes per hour. With its columnar architecture, Vertica compresses data by up to 90% of disk space, so you can save on storage costs.
  • Hybrid cloud deployment freedom: Vertica allows you to deploy in any environment — public cloud, private cloud or hybrid cloud. With its flexible licensing options, you can choose the deployment model that fits your needs.
  • Extensive ecosystem upgrades: The platform integrates with the leading data science and BI tools.

Pros

  • Unified analytics platform
  • Fast processing of trillions of records
  • Can scale up to hundreds of nodes
  • Flexible licensing options
  • Vertica Unified Analytics Platform supports Kubernetes

Cons

  • Vertica Accelerator is only available in AWS Cloud

Pricing

Vertica-as-a-Service software pricing is based on the Vertica Consumption per Unit and the number of nodes. Additionally, pricing for Vertica Unified Analytics Platform has a pay as you go or licensing option.

Qubole

Qubole logo.
Image: Qubole

Qubole is a data lake company that provides a platform for machine learning, streaming and ad-hoc analytics. The platform offers data lake services like cloud infrastructure management, continuous data engineering, analytics and machine learning.

Key features

  • Ad-hoc analysis: The Workbench allows users to author, save, template and share reports and queries.
  • Data pipelines: Qubole’s Assisted Pipeline Builder enables users to build and schedule data pipelines using a visual interface.
  • Machine learning: The Machine Learning Service provides a platform with a multi-language interpreter, offline editing and version control capabilities.
  • Data engineering: Qubole’s Data Engineering Service automates end-to-end pipelines and helps users avoid data ingestion and preparation bottlenecks.
  • Platform Runtime: The Platform Runtime is a cloud-native service that provides data lake services like cloud infrastructure management, continuous data engineering, analytics and machine learning.

Pros

  • Focuses on accelerating data lake adoption
  • Reduces time to value
  • Provides complete data lake services with very little administration
  • Easy learning curve

Cons

  • Qubole Notebooks can be arduous

Pricing

Qubole offers a full-featured 30-day trial. Pricing for the Enterprise edition starts at $0.168 per Qubole Compute Units per hour.

Key features of data intelligence solutions

From our analysis of the top big data intelligence solutions above, several key features emerge.

  • The ability to collect and process large amounts of data quickly
  • Distribute data across parallel computing clusters
  • Flexible deployment options
  • Organize the information so that it may be handled by system administrators and retrieved for analysis
  • Allow businesses to scale computing resources to the number necessary to store and process the data
  • Extensive ecosystem integration

Who uses data intelligence solutions?

Data intelligence solutions are used by various businesses and organizations in different industries to make better data-based decisions. These solutions are often used by:

Financial services

Data intelligence solutions help financial institutions to make sense of vast volumes of data. By analyzing this data, banks and other financial institutions can identify trends and patterns that they use to make better decisions about where to invest money and how to manage risk. Financial service companies can also use data intelligence solutions to detect fraudulent activity.

Healthcare

As big data has become increasingly prevalent in recent years, healthcare companies have been turning to data intelligence solutions to use this vast amount of information. Data intelligence software is helping to organize and make sense of big data sets, allowing healthcare organizations to identify trends and glean insights that would otherwise be hidden.

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For example, data intelligence solutions are used to speed up the drug discovery process, as they help identify patterns in past clinical trial data. In addition, data intelligence is used to improve patient care, for instance, by identifying which treatments are most effective for particular conditions.

Retail

Retailers have long been aware of the importance of data. However, it is only recently that big data and data intelligence solutions have become available to help them use this data. These solutions enable retailers to personalize the customer experience based on factors such as previous behavior and location.

Benefits of using data intelligence software

There are many benefits of using data intelligence software. Data intelligence solutions can help businesses to:

  • Make data-driven decisions: Data intelligence provides insights that would otherwise be hidden, allowing businesses to make better-informed decisions.
  • Improve customer experience: By understanding customer behavior, data intelligence solutions can help businesses to provide a more personalized experience.
  • Detect fraud: Data intelligence can detect fraudulent activity, helping businesses protect themselves from financial losses.
  • Save time and money: These solutions automate repetitive tasks, freeing up employees to focus on more strategic tasks.
  • Increased organization efficiency: Data intelligence solutions help businesses to organize their data, making it easier to retrieve and use.
  • Increased competitive advantage: The insights provided by data intelligence software helps companies gain a competitive edge.
  • Trusted and governed data: Finally, data governance is an important consideration when using data intelligence solutions. Data governance ensures that information is accurate and reliable and meets all compliance requirements.