Solutions Review’s listing of the best data lakehouses is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best data lakehouses based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.
The editors at Solutions Review have developed this resource to assist buyers in search of the best data lakehouses to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data lakehouses all in one place. We’ve also included introductory software tutorials straight from the source so you can see each solution in action.
Note: The best data lakehouses solutions are listed in alphabetical order.
The Best Data Lakehouses
Platform: Actian Avalanche
Description: Actian Avalanche’s hybrid design means users can query workloads across both on-prem and cloud environments, while Actian support for data federation means data does not need to be moved from its place of origin. Customers only need to use the compute and storage resources they need as well. Actian Avalanche also comes pre-packaged with connector support for more than 200 popular enterprise applications. The built-in integration control pane is featured in a single user interface that operates and is managed in the cloud.
Platform: AWS Data Lake
Description: Amazon Web Services offers a data lake solution that automatically configures the core AWS services necessary to tag, search, share, transform, analyze, and govern specific subsets of data across a company or with other external users. The solution deploys a console that users can access to search and browse available datasets for their business needs. The solution also includes a federated template that allows you to launch a version of the solution that is ready to integrate with Microsoft Active Directory.
Platform: Cloudera Data Platform
Description: The Cloudera Data Platform (CDP) manages and secures the data lifecycle across all major public clouds and the private cloud. The product optimizes workloads based on analytics and machine learning, enables users to view data lineage across any cloud and transient clusters, and features a single pane of glass across hybrid and multi-cloud environments. CDP can scale to petabytes of data and thousands of diverse users. It also lets you secure and govern platform data and metadata with integrated interfaces.
Platform: Databricks Unified Analytics Platform
Description: Databricks offers a cloud and Apache Spark-based unified analytics platform that combines data engineering and data science functionality. The product leverages an array of open-source languages and includes proprietary features for operationalization, performance, and real-time enablement on Amazon Web Services. A Data Science Workspace enables users to explore data and build models collaboratively. It also provides one-click access to preconfigured ML environments for augmented machine learning with popular frameworks.
Platform: Google Data Lake
Description: Google offers a fully-managed enterprise data warehouse for analytics via its BigQuery product. The solution is serverless and enables organizations to analyze any data by creating a logical data warehouse over managed, columnar storage, and data from object storage and spreadsheets. BigQuery captures data in real-time using a streaming ingestion feature, and it’s built atop the Google Cloud Platform. The product also provides users the ability to share insights via datasets, queries, spreadsheets and reports.
Platform: IBM Db2
Description: IBM Db2 enables customers to run low-latency transactions and real-time analytics for demanding workloads. Db2 can run microservices and AI workloads via a hybrid database methodology offering availability, built-in security, scalability, and intelligent automation. IBM’s included container operators automate time-consuming database tasks while utilizing advanced workload management automation and a machine learning-optimized query engine.
Platform: Azure Data Lake
Description: Microsoft Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. It also integrates with operational stores and data warehouses so you can extend current data applications. The solution touts enterprise-grade security, auditing, and support. It is built on YARN and designed for cloud environments.
Platform: Mongo DB Atlas
Description: MongoDB is a cross-platform document-oriented database. It is classified as a NoSQL database program and uses JSON-like documents with schema. The software is developed by MongoDB and licensed under the Server Side Public License. Key features include ad hoc queries, indexing, and real-time aggregation, as well as a document model that maps to the objects in your application code. MongoDB provides drivers for more than 10 languages, and the community has built dozens more.
Platform: Oracle Database
Description: Oracle’s suite of data management capabilities allows users to manage both traditional and new data sets on its cloud platform. The company also offers an autonomous data warehouse cloud with more than 2,000 SaaS applications. The platform runs the gamut of big data functionality, with support for data integration and analytics as well. Its other data management offerings include Oracle Big Data Cloud, Oracle Big Data Cloud Service, Oracle Big Data SQL Cloud Service, and Oracle NoSQL Database.
Platform: Redis Enterprise
Description: Redis Labs is best known for its Redis Enterprise, a database product that takes advantage of modern in-memory technologies like NVMe and Persistent Memory to provide deployment over cloud and on-prem data centers. The solution features native data structures and a variety of data modeling techniques such as streams, graphs, documents, and machine learning with a real-time search engine. Redis has also had considerable success entering strategic partnerships with vendors such as Pivotal and Red Hat.
Platform: Snowflake Cloud Data Platform
Description: Snowflake offers a cloud data warehouse built atop Amazon Web Services. The solution loads and optimizes data from virtually any source, both structured and unstructured, including JSON, Avro, and XML. Snowflake features broad support for standard SQL, and users can do updates, deletes, analytical functions, transactions, and complex joins as a result. The tool requires zero management and no infrastructure. The columnar database engine uses advanced optimizations to crunch data, process reports, and run analytics.
Platform: Teradata Vantage
Description: Teradata offers a broad spectrum of data management solutions that include database management, cloud data warehousing, and data warehouse appliances. The company’s product portfolio is available on its own managed cloud and on Amazon Web Services and Microsoft Azure. Teradata provides organizations the ability to run diverse queries, in-database analytics, and complex workload management.
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