Alternatives to IBM watsonx.data
Compare IBM watsonx.data alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to IBM watsonx.data in 2026. Compare features, ratings, user reviews, pricing, and more from IBM watsonx.data competitors and alternatives in order to make an informed decision for your business.
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Teradata VantageCloud
Teradata
Teradata VantageCloud: The complete cloud analytics and data platform for AI. Teradata VantageCloud is an enterprise-grade, cloud-native data and analytics platform that unifies data management, advanced analytics, and AI/ML capabilities in a single environment. Designed for scalability and flexibility, VantageCloud supports multi-cloud and hybrid deployments, enabling organizations to manage structured and semi-structured data across AWS, Azure, Google Cloud, and on-premises systems. It offers full ANSI SQL support, integrates with open-source tools like Python and R, and provides built-in governance for secure, trusted AI. VantageCloud empowers users to run complex queries, build data pipelines, and operationalize machine learning models—all while maintaining interoperability with modern data ecosystems. -
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AnalyticsCreator
AnalyticsCreator
AnalyticsCreator is a metadata-driven data warehouse automation application for teams working in the Microsoft data ecosystem. It enables data engineers to design, generate, and maintain production-ready data products across Microsoft SQL Server, Azure Data Factory, and Microsoft Fabric. By using centralized metadata, AnalyticsCreator generates ELT pipelines, dimensional models, historization logic, and analytical models in a consistent, version-controlled way. This reduces manual implementation effort and tool sprawl while ensuring transparency through built-in lineage tracking and clear visibility into data dependencies and change impact. With CI/CD integration via Azure DevOps and GitHub, plus support for custom SQL, AnalyticsCreator helps data teams scale delivery, enforce standards, and maintain control as complexity grows. -
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Snowflake
Snowflake
Snowflake is a comprehensive AI Data Cloud platform designed to eliminate data silos and simplify data architectures, enabling organizations to get more value from their data. The platform offers interoperable storage that provides near-infinite scale and access to diverse data sources, both inside and outside Snowflake. Its elastic compute engine delivers high performance for any number of users, workloads, and data volumes with seamless scalability. Snowflake’s Cortex AI accelerates enterprise AI by providing secure access to leading large language models (LLMs) and data chat services. The platform’s cloud services automate complex resource management, ensuring reliability and cost efficiency. Trusted by over 11,000 global customers across industries, Snowflake helps businesses collaborate on data, build data applications, and maintain a competitive edge.Starting Price: $2 compute/month -
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Amazon Redshift
Amazon
More customers pick Amazon Redshift than any other cloud data warehouse. Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. Companies like Lyft have grown with Redshift from startups to multi-billion dollar enterprises. No other data warehouse makes it as easy to gain new insights from all your data. With Redshift you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL. Redshift lets you easily save the results of your queries back to your S3 data lake using open formats like Apache Parquet to further analyze from other analytics services like Amazon EMR, Amazon Athena, and Amazon SageMaker. Redshift is the world’s fastest cloud data warehouse and gets faster every year. For performance intensive workloads you can use the new RA3 instances to get up to 3x the performance of any cloud data warehouse.Starting Price: $0.25 per hour -
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BigLake
Google
BigLake is a storage engine that unifies data warehouses and lakes by enabling BigQuery and open-source frameworks like Spark to access data with fine-grained access control. BigLake provides accelerated query performance across multi-cloud storage and open formats such as Apache Iceberg. Store a single copy of data with uniform features across data warehouses & lakes. Fine-grained access control and multi-cloud governance over distributed data. Seamless integration with open-source analytics tools and open data formats. Unlock analytics on distributed data regardless of where and how it’s stored, while choosing the best analytics tools, open source or cloud-native over a single copy of data. Fine-grained access control across open source engines like Apache Spark, Presto, and Trino, and open formats such as Parquet. Performant queries over data lakes powered by BigQuery. Integrates with Dataplex to provide management at scale, including logical data organization.Starting Price: $5 per TB -
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A data lakehouse is a modern, open architecture that enables you to store, understand, and analyze all your data. It combines the power and richness of data warehouses with the breadth and flexibility of the most popular open source data technologies you use today. A data lakehouse can be built from the ground up on Oracle Cloud Infrastructure (OCI) to work with the latest AI frameworks and prebuilt AI services like Oracle’s language service. Data Flow is a serverless Spark service that enables our customers to focus on their Spark workloads with zero infrastructure concepts. Oracle customers want to build advanced, machine learning-based analytics over their Oracle SaaS data, or any SaaS data. Our easy- to-use data integration connectors for Oracle SaaS, make creating a lakehouse to analyze all data with your SaaS data easy and reduces time to solution.
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CelerData Cloud
CelerData
CelerData is a high-performance SQL engine built to power analytics directly on data lakehouses, eliminating the need for traditional data‐warehouse ingestion pipelines. It delivers sub-second query performance at scale, supports on-the‐fly JOINs without costly denormalization, and simplifies architecture by allowing users to run demanding workloads on open format tables. Built on the open source engine StarRocks, the platform outperforms legacy query engines like Trino, ClickHouse, and Apache Druid in latency, concurrency, and cost-efficiency. With a cloud-managed service that runs in your own VPC, you retain infrastructure control and data ownership while CelerData handles maintenance and optimization. The platform is positioned to power real-time OLAP, business intelligence, and customer-facing analytics use cases and is trusted by enterprise customers (including names such as Pinterest, Coinbase, and Fanatics) who have achieved significant latency reductions and cost savings. -
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Presto
Presto Foundation
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. For data engineers who struggle with managing multiple query languages and interfaces to siloed databases and storage, Presto is the fast and reliable engine that provides one simple ANSI SQL interface for all your data analytics and your open lakehouse. Different engines for different workloads means you will have to re-platform down the road. With Presto, you get 1 familar ANSI SQL language and 1 engine for your data analytics so you don't need to graduate to another lakehouse engine. Presto can be used for interactive and batch workloads, small and large amounts of data, and scales from a few to thousands of users. Presto gives you one simple ANSI SQL interface for all of your data in various siloed data systems, helping you join your data ecosystem together. -
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Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
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Onehouse
Onehouse
The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion. -
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e6data
e6data
Limited competition due to deep barriers to entry, specialized know-how, massive capital needs, and long time-to-market. Existing platforms are indistinguishable in price, and performance reducing the incentive to switch. Migrating from one engine’s SQL dialect to another engine’s SQL involves months of effort. Truly format-neutral computing, interoperable with all major open standards. Enterprise data leaders are hit by an unprecedented explosion in computing demand for data intelligence. They are surprised to find that 10% of their heavy, compute-intensive use cases consume 80% of the cost, engineering effort and stakeholder complaints. Unfortunately, such workloads are also mission-critical and non-discretionary. e6data amplifies ROI on enterprises' existing data platforms and architecture. e6data’s truly format-neutral compute has the unique distinction of being equally efficient and performant across leading data lakehouse table formats. -
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Archon Data Store
Platform 3 Solutions
Archon Data Store is a next-generation enterprise data archiving platform designed to help organizations manage rapid data growth, reduce legacy application costs, and meet global compliance standards. Built on a modern Lakehouse architecture, Archon Data Store unifies data lakes and data warehouses to deliver secure, scalable, and analytics-ready archival storage. The platform supports on-premise, cloud, and hybrid deployments with AES-256 encryption, audit trails, metadata governance, and role-based access control. Archon Data Store offers intelligent storage tiering, high-performance querying, and seamless integration with BI tools. It enables efficient application decommissioning, cloud migration, and digital modernization while transforming archived data into a strategic asset. With Archon Data Store, organizations can ensure long-term compliance, optimize storage costs, and unlock AI-driven insights from historical data. -
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Dremio
Dremio
Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable. -
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DataLakeHouse.io
DataLakeHouse.io
DataLakeHouse.io (DLH.io) Data Sync provides replication and synchronization of operational systems (on-premise and cloud-based SaaS) data into destinations of their choosing, primarily Cloud Data Warehouses. Built for marketing teams and really any data team at any size organization, DLH.io enables business cases for building single source of truth data repositories, such as dimensional data warehouses, data vault 2.0, and other machine learning workloads. Use cases are technical and functional including: ELT, ETL, Data Warehouse, Pipeline, Analytics, AI & Machine Learning, Data, Marketing, Sales, Retail, FinTech, Restaurant, Manufacturing, Public Sector, and more. DataLakeHouse.io is on a mission to orchestrate data for every organization particularly those desiring to become data-driven, or those that are continuing their data driven strategy journey. DataLakeHouse.io (aka DLH.io) enables hundreds of companies to managed their cloud data warehousing and analytics solutions.Starting Price: $99 -
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Delta Lake
Delta Lake
Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions. Delta Lake brings ACID transactions to your data lakes. It provides serializability, the strongest level of isolation level. Learn more at Diving into Delta Lake: Unpacking the Transaction Log. In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata. As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments. -
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Qubole
Qubole
Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. No other platform offers the openness and data workload flexibility of Qubole while lowering cloud data lake costs by over 50 percent. Qubole delivers faster access to petabytes of secure, reliable and trusted datasets of structured and unstructured data for Analytics and Machine Learning. Users conduct ETL, analytics, and AI/ML workloads efficiently in end-to-end fashion across best-of-breed open source engines, multiple formats, libraries, and languages adapted to data volume, variety, SLAs and organizational policies. -
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FutureAnalytica
FutureAnalytica
Ours is the world’s first & only end-to-end platform for all your AI-powered innovation needs — right from data cleansing & structuring, to creating & deploying advanced data-science models, to infusing advanced analytics algorithms with built-in Recommendation AI, to deducing the outcomes with easy-to-deduce visualization dashboards, as well as Explainable AI to backtrack how the outcomes were derived, our no-code AI platform can do it all! Our platform offers a holistic, seamless data science experience. With key features like a robust Data Lakehouse, a unique AI Studio, a comprehensive AI Marketplace, and a world-class data-science support team (on a need basis), FutureAnalytica is geared to reduce your time, efforts & costs across your data-science & AI journey. Initiate discussions with the leadership, followed by a quick technology assessment in 1–3 days. Build ready-to-integrate AI solutions using FA's fully automated data science & AI platform in 10–18 days. -
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Cloudera
Cloudera
Manage and secure the data lifecycle from the Edge to AI in any cloud or data center. Operates across all major public clouds and the private cloud with a public cloud experience everywhere. Integrates data management and analytic experiences across the data lifecycle for data anywhere. Delivers security, compliance, migration, and metadata management across all environments. Open source, open integrations, extensible, & open to multiple data stores and compute architectures. Deliver easier, faster, and safer self-service analytics experiences. Provide self-service access to integrated, multi-function analytics on centrally managed and secured business data while deploying a consistent experience anywhere—on premises or in hybrid and multi-cloud. Enjoy consistent data security, governance, lineage, and control, while deploying the powerful, easy-to-use cloud analytics experiences business users require and eliminating their need for shadow IT solutions. -
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Lyftrondata
Lyftrondata
Whether you want to build a governed delta lake, data warehouse, or simply want to migrate from your traditional database to a modern cloud data warehouse, do it all with Lyftrondata. Simply create and manage all of your data workloads on one platform by automatically building your pipeline and warehouse. Analyze it instantly with ANSI SQL, BI/ML tools, and share it without worrying about writing any custom code. Boost the productivity of your data professionals and shorten your time to value. Define, categorize, and find all data sets in one place. Share these data sets with other experts with zero codings and drive data-driven insights. This data sharing ability is perfect for companies that want to store their data once, share it with other experts, and use it multiple times, now and in the future. Define dataset, apply SQL transformations or simply migrate your SQL data processing logic to any cloud data warehouse. -
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Qlik Compose
Qlik
Qlik Compose for Data Warehouses provides a modern approach by automating and optimizing data warehouse creation and operation. Qlik Compose automates designing the warehouse, generating ETL code, and quickly applying updates, all whilst leveraging best practices and proven design patterns. Qlik Compose for Data Warehouses dramatically reduces the time, cost and risk of BI projects, whether on-premises or in the cloud. Qlik Compose for Data Lakes automates your data pipelines to create analytics-ready data sets. By automating data ingestion, schema creation, and continual updates, organizations realize faster time-to-value from their existing data lake investments. -
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IOMETE
IOMETE
IOMETE is a self-hosted data lakehouse platform built on Apache Iceberg, Apache Spark, and Kubernetes. Run it on-premises or in your private cloud — your infrastructure, your data, your control. Built for enterprises in regulated industries, IOMETE eliminates third-party ICT risk at the data layer by architecture — not by contract. No SaaS dependencies. No data leaving your perimeter. Compliance with GDPR, DORA, and NIS2 is structural, not contractual. Included in one platform: - Data Lakehouse(s) - Data Catalog - SQL Editor - Apache Spark Jobs - ML Notebooks - Orchestration Engine - Spark Connect Key capabilities: Apache Iceberg-native storage, Kubernetes-native deployment (K8s + OpenShift), row/column/tag-based access control, Data Mesh support, air-gapped and zero-trust compatible. Transparent pricing — CPU-based, no per-query fees, no billing surprises.Starting Price: Free -
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BryteFlow
BryteFlow
BryteFlow builds the most efficient automated environments for analytics ever. It converts Amazon S3 into an awesome analytics platform by leveraging the AWS ecosystem intelligently to deliver data at lightning speeds. It complements AWS Lake Formation and automates the Modern Data Architecture providing performance and productivity. You can completely automate data ingestion with BryteFlow Ingest’s simple point-and-click interface while BryteFlow XL Ingest is great for the initial full ingest for very large datasets. No coding is needed! With BryteFlow Blend you can merge data from varied sources like Oracle, SQL Server, Salesforce and SAP etc. and transform it to make it ready for Analytics and Machine Learning. BryteFlow TruData reconciles the data at the destination with the source continually or at a frequency you select. If data is missing or incomplete you get an alert so you can fix the issue easily. -
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Openbridge
Openbridge
Uncover insights to supercharge sales growth using code-free, fully-automated data pipelines to data lakes or cloud warehouses. A flexible, standards-based platform to unify sales and marketing data for automating insights and smarter growth. Say goodbye to messy, expensive manual data downloads. Always know what you’ll pay and only pay for what you use. Fuel your tools with quick access to analytics-ready data. As certified developers, we only work with secure, official APIs. Get started quickly with data pipelines from popular sources. Pre-built, pre-transformed, and ready-to-go data pipelines. Unlock data from Amazon Vendor Central, Amazon Seller Central, Instagram Stories, Facebook, Amazon Advertising, Google Ads, and many others. Code-free data ingestion and transformation processes allow teams to realize value from their data quickly and cost-effectively. Data is always securely stored directly in a trusted, customer-owned data destination like Databricks, Amazon Redshift, etc.Starting Price: $149 per month -
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Amazon Security Lake
Amazon
Amazon Security Lake automatically centralizes security data from AWS environments, SaaS providers, on-premises, and cloud sources into a purpose-built data lake stored in your account. With Security Lake, you can get a more complete understanding of your security data across your entire organization. You can also improve the protection of your workloads, applications, and data. Security Lake has adopted the Open Cybersecurity Schema Framework (OCSF), an open standard. With OCSF support, the service normalizes and combines security data from AWS and a broad range of enterprise security data sources. Use your preferred analytics tools to analyze your security data while retaining complete control and ownership over that data. Centralize data visibility from cloud and on-premises sources across your accounts and AWS Regions. Streamline your data management at scale by normalizing your security data to an open standard.Starting Price: $0.75 per GB per month -
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Mozart Data
Mozart Data
Mozart Data is the all-in-one modern data platform that makes it easy to consolidate, organize, and analyze data. Start making data-driven decisions by setting up a modern data stack in an hour - no engineering required. -
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VeloDB
VeloDB
Powered by Apache Doris, VeloDB is a modern data warehouse for lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within seconds. Storage engine with real-time upsert、append and pre-aggregation. Unparalleled performance in both real-time data serving and interactive ad-hoc queries. Not just structured but also semi-structured data. Not just real-time analytics but also batch processing. Not just run queries against internal data but also work as a federate query engine to access external data lakes and databases. Distributed design to support linear scalability. Whether on-premise deployment or cloud service, separation or integration of storage and compute, resource usage can be flexibly and efficiently adjusted according to workload requirements. Built on and fully compatible with open source Apache Doris. Support MySQL protocol, functions, and SQL for easy integration with other data tools. -
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Sesame Software
Sesame Software
Sesame Software specializes in secure, efficient data integration and replication across diverse cloud, hybrid, and on-premise sources. Our patented scalability ensures comprehensive access to critical business data, facilitating a holistic view in the BI tools of your choice. This unified perspective empowers your own robust reporting and analytics, enabling your organization to regain control of your data with confidence. At Sesame Software, we understand what’s at stake when you need to move a massive amount of data between environments quickly—while keeping it protected, maintaining centralized access, and ensuring compliance with regulations. Over the past 30+ years, we’ve helped hundreds of organizations like Proctor & Gamble, Bank of America, and the U.S. government connect, move, store, and protect their data. -
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Alibaba Cloud Data Lake Formation
Alibaba Cloud
A data lake is a centralized repository used for big data and AI computing. It allows you to store structured and unstructured data at any scale. Data Lake Formation (DLF) is a key component of the cloud-native data lake framework. DLF provides an easy way to build a cloud-native data lake. It seamlessly integrates with a variety of compute engines and allows you to manage the metadata in data lakes in a centralized manner and control enterprise-class permissions. Systematically collects structured, semi-structured, and unstructured data and supports massive data storage. Uses an architecture that separates computing from storage. You can plan resources on demand at low costs. This improves data processing efficiency to meet the rapidly changing business requirements. DLF can automatically discover and collect metadata from multiple engines and manage the metadata in a centralized manner to solve the data silo issues. -
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Kylo
Teradata
Kylo is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects. Self-service data ingest with data cleansing, validation, and automatic profiling. Wrangle data with visual sql and an interactive transform through a simple user interface. Search and explore data and metadata, view lineage, and profile statistics. Monitor health of feeds and services in the data lake. Track SLAs and troubleshoot performance. Design batch or streaming pipeline templates in Apache NiFi and register with Kylo to enable user self-service. Organizations can expend significant engineering effort moving data into Hadoop yet struggle to maintain governance and data quality. Kylo dramatically simplifies data ingest by shifting ingest to data owners through a simple guided UI. -
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Narrative
Narrative
Create new streams of revenue using the data you already collect with your own branded data shop. Narrative is focused on the fundamental principles that make buying and selling data easier, safer, and more strategic. Ensure that the data you access meets your standards, whatever they may be. Know exactly who you’re working with and how the data was collected. Easily access new supply and demand for a more agile and accessible data strategy. Own your data strategy entirely with end-to-end control of inputs and outputs. Our platform simplifies and automates the most time- and labor-intensive aspects of data acquisition, so you can access new data sources in days, not months. With filters, budget controls, and automatic deduplication, you’ll only ever pay for the data you need, and nothing that you don’t.Starting Price: $0 -
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lakeFS
Treeverse
lakeFS enables you to manage your data lake the way you manage your code. Run parallel pipelines for experimentation and CI/CD for your data. Simplifying the lives of engineers, data scientists and analysts who are transforming the world with data. lakeFS is an open source platform that delivers resilience and manageability to object-storage based data lakes. With lakeFS you can build repeatable, atomic and versioned data lake operations, from complex ETL jobs to data science and analytics. lakeFS supports AWS S3, Azure Blob Storage and Google Cloud Storage (GCS) as its underlying storage service. It is API compatible with S3 and works seamlessly with all modern data frameworks such as Spark, Hive, AWS Athena, Presto, etc. lakeFS provides a Git-like branching and committing model that scales to exabytes of data by utilizing S3, GCS, or Azure Blob for storage. -
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Talend Data Fabric
Qlik
Talend Data Fabric’s suite of cloud services efficiently handles all your integration and integrity challenges — on-premises or in the cloud, any source, any endpoint. Deliver trusted data at the moment you need it — for every user, every time. Ingest and integrate data, applications, files, events and APIs from any source or endpoint to any location, on-premise and in the cloud, easier and faster with an intuitive interface and no coding. Embed quality into data management and guarantee ironclad regulatory compliance with a thoroughly collaborative, pervasive and cohesive approach to data governance. Make the most informed decisions based on high quality, trustworthy data derived from batch and real-time processing and bolstered with market-leading data cleaning and enrichment tools. Get more value from your data by making it available internally and externally. Extensive self-service capabilities make building APIs easy— improve customer engagement. -
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Datametica
Datametica
At Datametica, our birds with unprecedented capabilities help eliminate business risks, cost, time, frustration, and anxiety from the entire process of data warehouse migration to the cloud. Migration of existing data warehouse, data lake, ETL, and Enterprise business intelligence to the cloud environment of your choice using Datametica automated product suite. Architecting an end-to-end migration strategy, with workload discovery, assessment, planning, and cloud optimization. Starting from discovery and assessment of your existing data warehouse to planning the migration strategy – Eagle gives clarity on what’s needed to be migrated and in what sequence, how the process can be streamlined, and what are the timelines and costs. The holistic view of the workloads and planning reduces the migration risk without impacting the business. -
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Apache Doris
The Apache Software Foundation
Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation. Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine. Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL. Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches. Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.Starting Price: Free -
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Cloudera Data Warehouse
Cloudera
Cloudera Data Warehouse is a cloud-native, self-service analytics solution that lets IT rapidly deliver query capabilities to BI analysts, enabling users to go from zero to query in minutes. It supports all data types, structured, semi-structured, unstructured, real-time, and batch, and scales cost-effectively from gigabytes to petabytes. It is fully integrated with streaming, data engineering, and AI services, and enforces a unified security, governance, and metadata framework across private, public, or hybrid cloud deployments. Each virtual warehouse (data warehouse or mart) is isolated and automatically configured and optimized, ensuring that workloads do not interfere with each other. Cloudera leverages open source engines such as Hive, Impala, Kudu, and Druid, along with tools like Hue and more, to handle diverse analytics, from dashboards and operational analytics to research and discovery over vast event or time-series data. -
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MinIO
MinIO
MinIO's high-performance object storage suite is software defined and enables customers to build cloud-native data infrastructure for machine learning, analytics and application data workloads. MinIO object storage is fundamentally different. Designed for performance and the S3 API, it is 100% open-source. MinIO is ideal for large, private cloud environments with stringent security requirements and delivers mission-critical availability across a diverse range of workloads. MinIO is the world's fastest object storage server. With READ/WRITE speeds of 183 GB/s and 171 GB/s on standard hardware, object storage can operate as the primary storage tier for a diverse set of workloads ranging from Spark, Presto, TensorFlow, H2O.ai as well as a replacement for Hadoop HDFS. MinIO leverages the hard won knowledge of the web scalers to bring a simple scaling model to object storage. At MinIO, scaling starts with a single cluster which can be federated with other MinIO clusters. -
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The Qlik Data Integration platform for managed data lakes automates the process of providing continuously updated, accurate, and trusted data sets for business analytics. Data engineers have the agility to quickly add new sources and ensure success at every step of the data lake pipeline from real-time data ingestion, to refinement, provisioning, and governance. A simple and universal solution for continually ingesting enterprise data into popular data lakes in real-time. A model-driven approach for quickly designing, building, and managing data lakes on-premises or in the cloud. Deliver a smart enterprise-scale data catalog to securely share all of your derived data sets with business users.
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Kinetica
Kinetica
A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale. -
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IBM® Db2® Warehouse provides a client-managed, preconfigured data warehouse that runs in private clouds, virtual private clouds and other container-supported infrastructures. It is designed to be the ideal hybrid cloud solution when you must maintain control of your data but want cloud-like flexibility. With built-in machine learning, automated scaling, built-in analytics, and SMP and MPP processing, Db2 Warehouse enables you to bring AI to your business faster and easier. Deploy a pre-configured data warehouse in minutes on your supported infrastructure of choice with elastic scaling for easier updates and upgrades. Apply in-database analytics where the data resides, allowing enterprise AI to operate faster and more efficiently. Write your application once and move that workload to the right location, whether public cloud, private cloud or on-premises — with minimal or no changes required.
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IBM Storage Scale is software-defined file and object storage that enables organizations to build a global data platform for artificial intelligence (AI), high-performance computing (HPC), advanced analytics, and other demanding workloads. Unlike traditional applications that work with structured data, today’s performance-intensive AI and analytics workloads operate on unstructured data, such as documents, audio, images, videos, and other objects. IBM Storage Scale software provides global data abstraction services that seamlessly connect multiple data sources across multiple locations, including non-IBM storage environments. It’s based on a massively parallel file system and can be deployed on multiple hardware platforms including x86, IBM Power, IBM zSystem mainframes, ARM-based POSIX client, virtual machines, and Kubernetes.Starting Price: $19.10 per terabyte
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OpenText Analytics Database is a high-performance, scalable analytics platform that enables organizations to analyze massive data sets quickly and cost-effectively. It supports real-time analytics and in-database machine learning to deliver actionable business insights. The platform can be deployed flexibly across hybrid, multi-cloud, and on-premises environments to optimize infrastructure and reduce total cost of ownership. Its massively parallel processing (MPP) architecture handles complex queries efficiently, regardless of data size. OpenText Analytics Database also features compatibility with data lakehouse architectures, supporting formats like Parquet and ORC. With built-in machine learning and broad language support, it empowers users from SQL experts to Python developers to derive predictive insights.
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dashDB Local
IBM
As the newest edition to the IBM dashDB family, dashDB Local rounds out IBM's hybrid data warehouse strategy, providing organizations the most flexible architecture needed to lower the cost model of analytics in the dynamic world of big data and the cloud. How is this possible? Through a common analytics engine, with different deployment options across private and public clouds, analytics workloads can be moved and optimized with ease. dashDB Local is now an option when you prefer deployment on a hosted private cloud or on-premises private cloud through a software-defined infrastructure. From an IT standpoint, dashDB Local simplifies deployment and management through container technology, with elastic scaling and easy maintenance. From a user standpoint, dashDB Local provides the speed needed to quickly cycle through the process of data acquisition, applies the right analytics to meet a specific use case, and operationalizes the insights. -
43
Data Lakes on AWS
Amazon
Many Amazon Web Services (AWS) customers require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is a new and increasingly popular way to store and analyze data because it allows companies to manage multiple data types from a wide variety of sources, and store this data, structured and unstructured, in a centralized repository. The AWS Cloud provides many of the building blocks required to help customers implement a secure, flexible, and cost-effective data lake. These include AWS managed services that help ingest, store, find, process, and analyze both structured and unstructured data. To support our customers as they build data lakes, AWS offers the data lake solution, which is an automated reference implementation that deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets. -
44
ELCA Smart Data Lake Builder
ELCA Group
Classical Data Lakes are often reduced to basic but cheap raw data storage, neglecting significant aspects like transformation, data quality and security. These topics are left to data scientists, who end up spending up to 80% of their time acquiring, understanding and cleaning data before they can start using their core competencies. In addition, classical Data Lakes are often implemented by separate departments using different standards and tools, which makes it harder to implement comprehensive analytical use cases. Smart Data Lakes solve these various issues by providing architectural and methodical guidelines, together with an efficient tool to build a strong high-quality data foundation. Smart Data Lakes are at the core of any modern analytics platform. Their structure easily integrates prevalent Data Science tools and open source technologies, as well as AI and ML. Their storage is cheap and scalable, supporting both unstructured data and complex data structures.Starting Price: Free -
45
Hydrolix
Hydrolix
Hydrolix is a streaming data lake that combines decoupled storage, indexed search, and stream processing to deliver real-time query performance at terabyte-scale for a radically lower cost. CFOs love the 4x reduction in data retention costs. Product teams love 4x more data to work with. Spin up resources when you need them and scale to zero when you don’t. Fine-tune resource consumption and performance by workload to control costs. Imagine what you can build when you don’t have to sacrifice data because of budget. Ingest, enrich, and transform log data from multiple sources including Kafka, Kinesis, and HTTP. Return just the data you need, no matter how big your data is. Reduce latency and costs, eliminate timeouts, and brute force queries. Storage is decoupled from ingest and query, allowing each to independently scale to meet performance and budget targets. Hydrolix’s high-density compression (HDX) typically reduces 1TB of stored data to 55GB.Starting Price: $2,237 per month -
46
Databend
Databend
Databend is a modern, cloud-native data warehouse built to deliver high-performance, cost-efficient analytics for large-scale data processing. It is designed with an elastic architecture that scales dynamically to meet the demands of different workloads, ensuring efficient resource utilization and lower operational costs. Written in Rust, Databend offers exceptional performance through features like vectorized query execution and columnar storage, which optimize data retrieval and processing speeds. Its cloud-first design enables seamless integration with cloud platforms, and it emphasizes reliability, data consistency, and fault tolerance. Databend is an open source solution, making it a flexible and accessible choice for data teams looking to handle big data analytics in the cloud.Starting Price: Free -
47
Amazon EMR
Amazon
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting. -
48
AWS Lake Formation
Amazon
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake lets you break down data silos and combine different types of analytics to gain insights and guide better business decisions. Setting up and managing data lakes today involves a lot of manual, complicated, and time-consuming tasks. This work includes loading data from diverse sources, monitoring those data flows, setting up partitions, turning on encryption and managing keys, defining transformation jobs and monitoring their operation, reorganizing data into a columnar format, deduplicating redundant data, and matching linked records. Once data has been loaded into the data lake, you need to grant fine-grained access to datasets, and audit access over time across a wide range of analytics and machine learning (ML) tools and services. -
49
Infor Data Lake
Infor
Solving today’s enterprise and industry challenges requires big data. The ability to capture data from across your enterprise—whether generated by disparate applications, people, or IoT infrastructure–offers tremendous potential. Infor’s Data Lake tools deliver schema-on-read intelligence along with a fast, flexible data consumption framework to enable new ways of making key decisions. With leveraged access to your entire Infor ecosystem, you can start capturing and delivering big data to power your next generation analytics and machine learning strategies. Infinitely scalable, the Infor Data Lake provides a unified repository for capturing all of your enterprise data. Grow with your insights and investments, ingest more content for better informed decisions, improve your analytics profiles, and provide rich data sets to build more powerful machine learning processes. -
50
Stackable
Stackable
The Stackable data platform was designed with openness and flexibility in mind. It provides you with a curated selection of the best open source data apps like Apache Kafka, Apache Druid, Trino, and Apache Spark. While other current offerings either push their proprietary solutions or deepen vendor lock-in, Stackable takes a different approach. All data apps work together seamlessly and can be added or removed in no time. Based on Kubernetes, it runs everywhere, on-prem or in the cloud. stackablectl and a Kubernetes cluster are all you need to run your first stackable data platform. Within minutes, you will be ready to start working with your data. Configure your one-line startup command right here. Similar to kubectl, stackablectl is designed to easily interface with the Stackable Data Platform. Use the command line utility to deploy and manage stackable data apps on Kubernetes. With stackablectl, you can create, delete, and update components.Starting Price: Free
