Alternatives to Apache DataFusion
Compare Apache DataFusion alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Apache DataFusion in 2026. Compare features, ratings, user reviews, pricing, and more from Apache DataFusion competitors and alternatives in order to make an informed decision for your business.
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StarTree
StarTree
StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.Starting Price: Free -
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OpenObserve
OpenObserve
OpenObserve is an open source observability platform for logs, metrics, and traces that emphasizes high performance, scalability, and dramatically lower cost. It supports petabyte-scale observability thanks to features like data compression using columnar storage and the ability to use “bring your own bucket” storage (local disk, S3, GCS, Azure Blob, etc.). It is written in Rust, uses the DataFusion query engine to directly query Parquet files, and provides a stateless, horizontally scalable architecture with caching (both result and disk) to maintain speed under heavy load. It embraces open standards (OpenTelemetry compatibility, vendor-neutral APIs), so it fits into existing monitoring/logging workflows. Key modules include logs, metrics, traces, frontend monitoring, pipelines, alerts, and dashboards/visualizations.Starting Price: $0.30 per GB -
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PySpark
PySpark
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. -
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GeoSpock
GeoSpock
GeoSpock enables data fusion for the connected world with GeoSpock DB – the space-time analytics database. GeoSpock DB is a unique, cloud-native database optimised for querying for real-world use cases, able to fuse multiple sources of Internet of Things (IoT) data together to unlock its full value, whilst simultaneously reducing complexity and cost. GeoSpock DB enables efficient storage, data fusion, and rapid programmatic access to data, and allows you to run ANSI SQL queries and connect to analytics tools via JDBC/ODBC connectors. Users are able to perform analysis and share insights using familiar toolsets, with support for common BI tools (such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™), and Data Science and Machine Learning environments (including Python Notebooks and Apache Spark). The database can also be integrated with internal applications and web services – with compatibility for open-source and visualisation libraries such as Kepler and Cesium.js. -
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Serverless, interactive querying for analyzing data in IBM Cloud Object Storage. Query your data directly where it is stored, there's no ETL, no databases, and no infrastructure to manage. IBM Cloud SQL Query uses Apache Spark, an open-source, fast, extensible, in-memory data processing engine optimized for low latency and ad hoc analysis of data. No ETL or schema definition needed to enable SQL queries. Analyze data where it sits in IBM Cloud Object Storage using our query editor and REST API. Run as many queries as you need; with pay-per-query pricing, you pay only for the data scan. Compress or partition data to drive savings and performance. IBM Cloud SQL Query is highly available and executes queries using compute resources across multiple facilities. IBM Cloud SQL Query supports a variety of data formats such as CSV, JSON and Parquet, and allows for standard ANSI SQL.Starting Price: $5.00/Terabyte-Month
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Polars
Polars
Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models. -
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Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
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SDF
SDF
SDF is a developer platform for data that enhances SQL comprehension across organizations, enabling data teams to unlock the full potential of their data. It provides a transformation layer to streamline query writing and management, an analytical database engine for local execution, and an accelerator for improved transformation processes. SDF also offers proactive quality and governance features, including reports, contracts, and impact analysis, to ensure data integrity and compliance. By representing business logic as code, SDF facilitates the classification and management of data types, enhancing the clarity and maintainability of data models. It integrates seamlessly with existing data workflows, supporting various SQL dialects and cloud environments, and is designed to scale with the growing needs of data teams. SDF's open-core architecture, built on Apache DataFusion, allows for customization and extension, fostering a collaborative ecosystem for data development. -
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Tabular
Tabular
Tabular is an open table store from the creators of Apache Iceberg. Connect multiple computing engines and frameworks. Decrease query time and storage costs by up to 50%. Centralize enforcement of data access (RBAC) policies. Connect any query engine or framework, including Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python. Smart compaction, clustering, and other automated data services reduce storage costs and query times by up to 50%. Unify data access at the database or table. RBAC controls are simple to manage, consistently enforced, and easy to audit. Centralize your security down to the table. Tabular is easy to use plus it features high-powered ingestion, performance, and RBAC under the hood. Tabular gives you the flexibility to work with multiple “best of breed” compute engines based on their strengths. Assign privileges at the data warehouse database, table, or column level.Starting Price: $100 per month -
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Google Cloud Data Fusion
Google
Open core, delivering hybrid and multi-cloud integration. Data Fusion is built using open source project CDAP, and this open core ensures data pipeline portability for users. CDAP’s broad integration with on-premises and public cloud platforms gives Cloud Data Fusion users the ability to break down silos and deliver insights that were previously inaccessible. Integrated with Google’s industry-leading big data tools. Data Fusion’s integration with Google Cloud simplifies data security and ensures data is immediately available for analysis. Whether you’re curating a data lake with Cloud Storage and Dataproc, moving data into BigQuery for data warehousing, or transforming data to land it in a relational store like Cloud Spanner, Cloud Data Fusion’s integration makes development and iteration fast and easy. -
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Apache Geode
Apache
Build high-speed, data-intensive applications that elastically meet performance requirements at any scale. Take advantage of Apache Geode's unique technology that blends advanced techniques for data replication, partitioning and distributed processing. Apache Geode provides a database-like consistency model, reliable transaction processing and a shared-nothing architecture to maintain very low latency performance with high concurrency processing. Data can easily be partitioned (sharded) or replicated between nodes allowing performance to scale as needed. Durability is ensured through redundant in-memory copies and disk-based persistence. Super fast write-ahead-logging (WAL) persistence with a shared-nothing architecture that is optimized for fast parallel recovery of nodes or an entire cluster. -
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Apache Impala
Apache
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. For Apache Hive users, Impala utilizes the same metadata and ODBC driver. Like Hive, Impala supports SQL, so you don't have to worry about reinventing the implementation wheel. With Impala, more users, whether using SQL queries or BI applications, can interact with more data through a single repository and metadata stored from source through analysis.Starting Price: Free -
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Apache Druid
Druid
Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures. -
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IBM Db2 Event Store is a cloud-native database system that is designed to handle massive amounts of structured data that is stored in Apache Parquet format. Because it is optimized for event-driven data processing and analysis, this high-speed data store can capture, analyze, and store more than 250 billion events per day. The data store is flexible and scalable to adapt quickly to your changing business needs. With the Db2 Event Store service, you can create these data stores in your Cloud Pak for Data cluster so that you can govern the data and use it for more in-depth analysis. You need to rapidly ingest large amounts of streaming data (up to one million inserts per second per node) and use it for real-time analytics with integrated machine learning capabilities. Analyze incoming data from different medical devices in real time to provide better health outcomes for patients while providing cost savings for moving the data to storage.
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HyperSQL DataBase
The hsql Development Group
HSQLDB (HyperSQL DataBase) is the leading SQL relational database system written in Java. It offers a small, fast multithreaded and transactional database engine with in-memory and disk-based tables and supports embedded and server modes. It includes a powerful command line SQL tool and simple GUI query tools. HSQLDB supports the widest range of SQL Standard features seen in any open source database engine: SQL:2016 core language features and an extensive list of SQL:2016 optional features. It supports full Advanced ANSI-92 SQL with only two exceptions. Many extensions to the Standard, including syntax compatibility modes and features of other popular database engines, are also supported. -
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SelectDB
SelectDB
SelectDB is a modern data warehouse based on Apache Doris, which supports rapid query analysis on large-scale real-time data. From Clickhouse to Apache Doris, to achieve the separation of the lake warehouse and upgrade to the lake warehouse. The fast-hand OLAP system carries nearly 1 billion query requests every day to provide data services for multiple scenes. Due to the problems of storage redundancy, resource seizure, complicated governance, and difficulty in querying and adjustment, the original lake warehouse separation architecture was decided to introduce Apache Doris lake warehouse, combined with Doris's materialized view rewriting ability and automated services, to achieve high-performance data query and flexible data governance. Write real-time data in seconds, and synchronize flow data from databases and data streams. Data storage engine for real-time update, real-time addition, and real-time pre-polymerization.Starting Price: $0.22 per hour -
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AnySQL Maestro
SQL Maestro Group
AnySQL Maestro is the premier multi-purpose admin tool for database management, control and development. SQL Maestro Group offers complete database management and web development solutions for all the most popular database servers providing the highest performance, scalability and reliability to meet the requirements of today's database applications. Support of any database engine (SQL Server, MySQL, Access, etc.) Database designer, data management, editing, grouping, sorting and filtering abilities. Handy SQL Editor with code folding and multi-threading. Visual query builder, data export/import to/from the most popular formats. Powerful BLOB viewer/editor. The application also provides you with a powerful set of tools to edit and execute SQL scripts, build visual diagrams for numeric data, compose OLAP cubes, and much more. High-quality DB2 tools which are as easy in use as Windows explorer.Starting Price: $79 one-time payment -
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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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Amazon Data Firehose
Amazon
Easily capture, transform, and load streaming data. Create a delivery stream, select your destination, and start streaming real-time data with just a few clicks. Automatically provision and scale compute, memory, and network resources without ongoing administration. Transform raw streaming data into formats like Apache Parquet, and dynamically partition streaming data without building your own processing pipelines. Amazon Data Firehose provides the easiest way to acquire, transform, and deliver data streams within seconds to data lakes, data warehouses, and analytics services. To use Amazon Data Firehose, you set up a stream with a source, destination, and required transformations. Amazon Data Firehose continuously processes the stream, automatically scales based on the amount of data available, and delivers it within seconds. Select the source for your data stream or write data using the Firehose Direct PUT API.Starting Price: $0.075 per month -
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EntelliFusion
Teksouth
Teksouth’s EntelliFusion is a fully managed, end-to-end solution. Rather than piecing together several different platforms for data prep, data warehousing and governance, then deploying a great deal of IT resources to figure out how to make it all work; EntelliFusion's architecture provides a one-stop shop for outfitting an organizations data infrastructure. With EntelliFusion, data silos become centralized in a single platform for cross functional KPI's, creating holistic and powerful insights. EntelliFusion’s “military-born” technology has proven successful against the strenuous demands of the USA’s top echelon of military operations. In this capacity, it was massively scaled across the DOD for over twenty years. EntelliFusion is built on the latest Microsoft technologies and frameworks which allows it to be continually enhanced and innovated. It is data agnostic, infinitely scalable, and guarantees accuracy and performance to promote end-user tool adoption. -
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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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Oracle Real Application Clusters (RAC) is a unique, scale-everything, highly available database architecture that transparently scales both reads and writes for all workloads, including OLTP, analytics, AI vectors, SaaS, JSON, batch, text, graph, IoT, and in-memory. It effortlessly scales complex applications such as SAP, Oracle Fusion Applications, and Salesforce workloads. Oracle RAC delivers the lowest latency and highest throughput for all data needs through its unique fused cache across servers, ensuring ultrafast local data access. Parallelized workloads across all CPUs guarantee maximum throughput, and the integration of Oracle’s storage design enables seamless online storage expansion. Unlike other databases that depend on public cloud infrastructures, sharding, or read replicas for scalability, Oracle RAC guarantees the lowest latency and highest throughput out of the box.
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Huawei FusionCube
Huawei
Huawei’s FusionCube hyper-converged infrastructure brings compute, storage, network, virtualization, and management into one tightly integrated package to achieve high performance, low latency, and rapid deployment. FusionCube’s built-in distributed storage engines enable deep convergence of compute and storage. These Huawei-developed engines eliminate performance bottlenecks while allowing for flexible capacity expansion. FusionCube supports industry mainstream databases and virtualization software. Huawei FusionCube 1000 HyperVisor&Data is data storage infrastructure based on converged architecture. It pre-integrates a distributed storage engine, virtualization software, and cloud management software to support on-demand resource allocation and linear expansion. -
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Imply
Imply
Imply is a real-time analytics platform built on Apache Druid, designed to handle large-scale, high-performance OLAP (Online Analytical Processing) workloads. It offers real-time data ingestion, fast query performance, and the ability to perform complex analytical queries on massive datasets with low latency. Imply is tailored for organizations that need interactive analytics, real-time dashboards, and data-driven decision-making at scale. It provides a user-friendly interface for data exploration, along with advanced features such as multi-tenancy, fine-grained access controls, and operational insights. With its distributed architecture and scalability, Imply is well-suited for use cases in streaming data analytics, business intelligence, and real-time monitoring across industries. -
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PartiQL
PartiQL
PartiQL's extensions to SQL are easy to understand, treat nested data as first class citizens and compose seamlessly with each other and SQL. This enables intuitive filtering, joining and aggregation on the combination of structured, semistructured and nested datasets. PartiQL enables unified query access across multiple data stores and data formats by separating the syntax and semantics of a query from the underlying format of the data or the data store that is being accessed. It enables users to interact with data with or without regular schema. PartiQL syntax, semantics, the embeddable reference interpreter, CLI, test framework, and tests are licensed under the Apache License, version 2.0, allowing you to freely use, copy, and distribute your changes under the terms of your choice. -
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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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HStreamDB
EMQ
A streaming database is purpose-built to ingest, store, process, and analyze massive data streams. It is a modern data infrastructure that unifies messaging, stream processing, and storage to help get value out of your data in real-time. Ingest massive amounts of data continuously generated from various sources, such as IoT device sensors. Store millions of data streams reliably in a specially designed distributed streaming data storage cluster. Consume data streams in real-time as fast as from Kafka by subscribing to topics in HStreamDB. With the permanent data stream storage, you can playback and consume data streams anytime. Process data streams based on event-time with the same familiar SQL syntax you use to query data in a relational database. You can use SQL to filter, transform, aggregate, and even join multiple data streams.Starting Price: Free -
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Apache Hive
Apache Software Foundation
The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. -
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R2 SQL
Cloudflare
R2 SQL is Cloudflare’s serverless, distributed analytics query engine (currently in open beta) that enables you to run SQL queries over Apache Iceberg tables stored in R2 Data Catalog without needing to manage your own compute clusters. It is built to efficiently query large volumes of data by leveraging metadata pruning, partition-level statistics, file and row-group filtering, and Cloudflare’s globally distributed compute infrastructure to parallelize execution. The system works by integrating with R2 object storage and an Iceberg catalog layer, so you can ingest data via Cloudflare Pipelines into Iceberg tables, and then query that data with minimal overhead. Queries can be issued via the Wrangler CLI or HTTP API (with an API token granting permissions across R2 SQL, Data Catalog, and storage). During the open beta period, using R2 SQL itself is not billed, only storage and standard R2 operations incur charges.Starting Price: Free -
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Google Cloud Datastream
Google
Serverless and easy-to-use change data capture and replication service. Access to streaming data from MySQL, PostgreSQL, AlloyDB, SQL Server, and Oracle databases. Near real-time analytics in BigQuery. Easy-to-use setup with built-in secure connectivity for faster time-to-value. A serverless platform that automatically scales, with no resources to provision or manage. Log-based mechanism to reduce the load and potential disruption on source databases. Synchronize data across heterogeneous databases, storage systems, and applications reliably, with low latency, while minimizing impact on source performance. Get up and running fast with a serverless and easy-to-use service that seamlessly scales up or down, and has no infrastructure to manage. Connect and integrate data across your organization with the best of Google Cloud services like BigQuery, Spanner, Dataflow, and Data Fusion. -
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DBeaver Community
DBeaver Corp.
DBeaver Community is a free, open-source database management tool recommended for personal projects. Manage and explore SQL databases like MySQL, MariaDB, PostgreSQL, SQLite, Apache Family, and more. Connect to popular SQL databases (PostgreSQL, MySQL, SQL Server, Oracle, SQLite, etc.) SQL editor to create queries with syntax highlighting and autocompletion AI assistant in SQL for building queries through natural language SSL/TLS support for encrypted connections SSH tunneling for secure remote access Proxy configuration to work with databases behind a corporate firewall Session Manager to terminate, disconnect, view and search for sessions Lock Manager to monitor and manage database locks Entity Relationship Diagrams to interactively explore and visualize database structure Query manager to view all executed queries, including execution time, duration, affected rows, and errors. Data editor to view, edit, and filter dataStarting Price: $11/month -
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Upsolver
Upsolver
Upsolver makes it incredibly simple to build a governed data lake and to manage, integrate and prepare streaming data for analysis. Define pipelines using only SQL on auto-generated schema-on-read. Easy visual IDE to accelerate building pipelines. Add Upserts and Deletes to data lake tables. Blend streaming and large-scale batch data. Automated schema evolution and reprocessing from previous state. Automatic orchestration of pipelines (no DAGs). Fully-managed execution at scale. Strong consistency guarantee over object storage. Near-zero maintenance overhead for analytics-ready data. Built-in hygiene for data lake tables including columnar formats, partitioning, compaction and vacuuming. 100,000 events per second (billions daily) at low cost. Continuous lock-free compaction to avoid “small files” problem. Parquet-based tables for fast queries. -
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Greenplum
Greenplum Database
Greenplum Database® is an advanced, fully featured, open source data warehouse. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes. Greenplum Database® project is released under the Apache 2 license. We want to thank all our current community contributors and are interested in all new potential contributions. For the Greenplum Database community no contribution is too small, we encourage all types of contributions. An open-source massively parallel data platform for analytics, machine learning and AI. Rapidly create and deploy models for complex applications in cybersecurity, predictive maintenance, risk management, fraud detection, and many other areas. Experience the fully featured, integrated, open source analytics platform. -
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LogFusion
Binary Fortress Software
LogFusion is a powerful realtime log monitoring application designed for system administrators and developers! Use custom highlighting rules, filtering and more. You can even sync your LogFusion settings between computers. Use LogFusion's powerful custom highlighting to match text strings or regex patterns and format the matched log lines to suit your needs. Use LogFusion's Advanced Text Filtering to filter and hide lines that don't match your search text, all in realtime as new lines are being added. Complex queries allow you to easily narrow down your results. LogFusion can automatically add new logs from Watched Folders. Just specify the folders to monitor, and LogFusion will automatically open any new log files created in those folders. -
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DeltaStream
DeltaStream
DeltaStream is a unified serverless stream processing platform that integrates with streaming storage services. Think about it as the compute layer on top of your streaming storage. It provides functionalities of streaming analytics(Stream processing) and streaming databases along with additional features to provide a complete platform to manage, process, secure and share streaming data. DeltaStream provides a SQL based interface where you can easily create stream processing applications such as streaming pipelines, materialized views, microservices and many more. It has a pluggable processing engine and currently uses Apache Flink as its primary stream processing engine. DeltaStream is more than just a query processing layer on top of Kafka or Kinesis. It brings relational database concepts to the data streaming world, including namespacing and role based access control enabling you to securely access, process and share your streaming data regardless of where they are stored. -
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PeerDB
PeerDB
If Postgres is at the core of your business and is a major source of data, PeerDB provides a fast, simple, and cost-effective way to replicate data from Postgres to data warehouses, queues, and storage. Designed to run at any scale, and tailored for data stores. PeerDB uses replication messages from the Postgres replication slot to replay the schema messages. Alerts for slot growth and connections. Native support for Postgres toast columns and large JSONB columns for IoT. Optimized query design to reduce warehouse costs; particularly useful for Snowflake and BigQuery. Support for partitioned tables via both publish. Blazing fast and consistent initial load by transaction snapshotting and CTID scans. High-availability, in-place upgrades, autoscaling, advance logs, metrics and monitoring dashboards, burstable instance types, and suitable for dev environments.Starting Price: $250 per month -
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Apache Arrow
The Apache Software Foundation
Apache Arrow defines a language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs. The Arrow memory format also supports zero-copy reads for lightning-fast data access without serialization overhead. Arrow's libraries implement the format and provide building blocks for a range of use cases, including high performance analytics. Many popular projects use Arrow to ship columnar data efficiently or as the basis for analytic engines. Apache Arrow is software created by and for the developer community. We are dedicated to open, kind communication and consensus decisionmaking. Our committers come from a range of organizations and backgrounds, and we welcome all to participate with us. -
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SlashDB
SlashDB
SlashDB automatically generates a REST API from relational databases for reading and writing in convenient formats like XML, JSON and CSV. Additionally, using HTML it allows for simply browsing through data so users can discover available databases, tables, queries and API endpoints. SlashDB is capable of providing a central access point to multiple databases, effectively forming an all-encompassing resource-oriented architecture (ROA), serving as an API gateway to the data for users that need access. SlashDB provides a web service shell for each of your database(s) by translating relational database structures into a logical library of uniform resource locators (URL). Conforming to REST methodology, every piece of data has its own unique identifying URL regardless of whether it is a simple scalar value or a nested structure of related records.Starting Price: $200 per database per month -
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Materialize
Materialize
Materialize is a reactive database that delivers incremental view updates. We help developers easily build with streaming data using standard SQL. Materialize can connect to many different external sources of data without pre-processing. Connect directly to streaming sources like Kafka, Postgres databases, CDC, or historical sources of data like files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are maintained and continually updated as new data streams in. With incrementally-updated views, developers can easily build data visualizations or real-time applications. Building with streaming data can be as simple as writing a few lines of SQL.Starting Price: $0.98 per hour -
40
Database Tour Pro
Vitaliy Levchenko Software
Here are some areas where Database Tour has competitive advantages among database tools in the marketplace. Preparing database queries in SQL editors with syntax highlighting, code completion, and Ctrl+clickable objects. Multiple windows with separate transactions. There is a possibility to execute multi-statement SQL scripts. Collecting SQL execution statistics for each query window. Performing useful operations with databases, such as searching and replacing text, batch data processing etc. Database Tour data grids give you the ability to view CLOB/MEMO (large text) and graphic data, expand rows, use autoscrolling, sorting, selecting group of records, copying data etc. Data in tables can be highlighted depending on its content (so called conditional formatting). There is a tool for working with BLOB fields, such as large text and graphics. -
41
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 -
42
Ververica
Ververica
Ververica Platform enables every enterprise to take advantage and derive immediate insight from its data in real-time. Powered by Apache Flink's robust streaming runtime, Ververica Platform makes this possible by providing an integrated solution for stateful stream processing and streaming analytics at scale. Powered by Apache Flink, Ververica Platform provides high throughput, low latency data processing, powerful abstractions and the operational flexibility trusted by some of the world’s largest and most successful data-driven enterprises such as Alibaba, Netflix and Uber. Ververica Platform brings the accumulated knowledge of our experience working with some of these large and innovative, data-driven companies into an easily-accessible, cost-effective and secure enterprise-ready platform. -
43
Prometheus
Prometheus
Power your metrics and alerting with a leading open-source monitoring solution. Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Besides stored time series, Prometheus may generate temporary derived time series as the result of queries. Prometheus provides a functional query language called PromQL (Prometheus Query Language) that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. Prometheus is configured via command-line flags and a configuration file. While the command-line flags configure immutable system parameters (such as storage locations, amount of data to keep on disk and in memory, etc.). Download: https://sourceforge.net/projects/prometheus.mirror/Starting Price: Free -
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Aqua Data Studio
AquaFold, an Idera, Inc. company
Aqua Data Studio is a multiple-platform, integrated development environment (IDE) for data. It provides benefits to a variety of data-centric roles, allowing them to manage a wide range of data sources. Aqua Data Studio provides scalable, cross-platform data management, supporting IT and data-centric specialists, including developers, database administrators, as well as data analysts, data modelers, and data architects. Simplifies tedious tasks involving SQL queries, data, result sets, schema, data models, files, instances, servers, as well as automation. Aqua Data Studio can be installed on the three popular operating systems: Microsoft Windows, Apple macOS, and Linux. The graphical user interface can display the nine of the most widely spoken languages: English, Spanish, French, German, Korean, Portuguese, Japanese, and Chinese. Aqua Data Studio supports over 40 of the most popular data source platforms, including relational, NoSQL, as well as managed cloud data sourceStarting Price: $499 per user per year -
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Google Cloud Lakehouse
Google
Google Cloud Lakehouse is a storage engine designed to unify data warehouses and data lakes into a single, cohesive platform. It allows organizations to access and manage data in open formats such as Apache Iceberg, Parquet, and ORC. The platform enables users to work with a single copy of data without needing to duplicate or move it across systems. It provides fine-grained security controls to ensure proper data governance and access management. Google Cloud Lakehouse simplifies data operations by integrating analytics and storage capabilities. It supports modern data workflows, including big data processing and analytics. The platform is built to scale with enterprise data needs while maintaining performance and flexibility. Overall, it helps organizations streamline data management and unlock insights more efficiently.Starting Price: $5 per TB -
46
ParadeDB
ParadeDB
ParadeDB brings column-oriented storage and vectorized query execution to Postgres tables. Users can choose between row and column-oriented storage at table creation time. Column-oriented tables are stored as Parquet files and are managed by Delta Lake. Search by keyword with BM25 scoring, configurable tokenizers, and multi-language support. Search by semantic meaning with support for sparse and dense vectors. Surface results with higher accuracy by combining the strengths of full text and similarity search. ParadeDB is ACID-compliant with concurrency controls across all transactions. ParadeDB integrates with the Postgres ecosystem, including clients, extensions, and libraries. -
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Managed Service for Apache Spark is a Google Cloud solution that simplifies running Apache Spark workloads with either serverless execution or fully managed clusters. It allows users to process large-scale data without needing to manage infrastructure, reducing operational complexity. The platform features Lightning Engine, which accelerates Spark performance by up to 4.9 times compared to open-source Spark. It supports data engineering, data science, and machine learning workflows at scale. Integration with Gemini enables AI-powered development, including automated code generation and troubleshooting. The service works seamlessly with open data formats like Apache Iceberg and integrates with tools like BigQuery and Knowledge Catalog. It offers flexible deployment options to suit different workloads and use cases. Overall, it provides a faster, smarter, and more efficient way to run Spark workloads in the cloud.
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CData Connect AI
CData
CData’s AI offering is centered on Connect AI and associated AI-driven connectivity capabilities, which provide live, governed access to enterprise data without moving it off source systems. Connect AI is built as a managed Model Context Protocol (MCP) platform that lets AI assistants, agents, copilots, and embedded AI applications directly query over 300 data sources, such as CRM, ERP, databases, APIs, with a full understanding of data semantics and relationships. It enforces source system authentication, respects existing role-based permissions, and ensures that AI actions (reads and writes) follow governance and audit rules. The system supports query pushdown, parallel paging, bulk read/write operations, streaming mode for large datasets, and cross-source reasoning via a unified semantic layer. In addition, CData’s “Talk to your Data” engine integrates with its Virtuality product to allow conversational access to BI insights and reports. -
49
ContentBox
Ortus Solutions
ContentBox is a professional open source (Apache 2 License) modular content management engine that allows you to easily build websites, blogs, wikis, complex web applications, and RESTFul web services. Built with a secure and flexible modular core, designed to scale, and combined with world-class support, ContentBox will get your projects out the door in no time. ContentBox CMS can be deployed to any ColdFusion/CFML engine or any Java Servlet Container. ContentBox is built with a solid open source MVC framework foundation; The ColdBox Platform, which has been powering ColdFusion/CFML applications since 2005 and used by thousands of developers worldwide. Used by clients like NASA, ESRI, Adobe TV, FAA, GE and many more. ContentBox has been designed using a rich Object Oriented content model powered by Hibernate, the de-facto standard Object Relational Mapper, and can run in any Java environment. Our entire infrastructure is built with scalability and cloud deployment in mind. -
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Trino
Trino
Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.Starting Price: Free
