Top 14 Open-source Free Data Warehouse Solutions for Enterprise
What is a Data warehouse Solution?
A data warehouse solution is a centralized repository designed for the storage, analysis, and retrieval of large volumes of structured and unstructured data from multiple sources. It consolidates data from various operational systems, transforming it into a unified format to support business intelligence activities, such as reporting, querying, and data mining.
Data warehouse apps enable organizations to gain insights by providing a historical context of data, facilitating trend analysis, and aiding decision-making processes. They typically involve processes like data extraction, transformation, and loading (ETL), and employ advanced technologies to handle complex queries efficiently.
Examples of data warehouse solutions include Amazon Redshift, Google BigQuery, and Snowflake, which offer scalable and performant environments for large-scale data analytics.
Use-cases of Data Warehouse Apps?
Data warehouse applications are essential in various industries for their ability to store and analyze large datasets. Here are some key use cases:
- Business Intelligence and Reporting:
- Data warehouse apps enable businesses to aggregate data from multiple sources, providing a comprehensive view of operations. This consolidated data supports the creation of detailed reports, dashboards, and visualizations, aiding in performance tracking, trend analysis, and strategic decision-making.
- Customer Relationship Management (CRM):
- By integrating customer data from various touchpoints, data warehouse apps help businesses understand customer behavior, preferences, and purchase patterns. This insight is crucial for personalized marketing campaigns, customer segmentation, and improving overall customer experience.
- Financial Analysis and Forecasting:
- Financial institutions use data warehouses to compile data from transactional systems, market feeds, and other sources. This enables detailed financial reporting, risk analysis, and forecasting, helping businesses manage budgets, investments, and compliance requirements effectively.
- Supply Chain Management:
- Data warehouses provide a unified view of supply chain operations, including inventory levels, shipment tracking, and supplier performance. This helps businesses optimize their supply chain processes, reduce costs, and improve delivery times.
- Healthcare Analytics:
- In healthcare, data warehouses aggregate patient records, treatment histories, and clinical data from various systems. This supports better patient care management, outcome analysis, and research, enabling healthcare providers to make data-driven decisions.
- Retail Analytics:
- Retailers use data warehouses to analyze sales data, track inventory, and monitor customer buying patterns. This helps in optimizing pricing strategies, managing stock levels, and improving customer satisfaction through targeted promotions.
- Compliance and Audit Reporting:
- Organizations can use data warehouses to maintain a secure and accurate record of transactions and activities for regulatory compliance. This ensures that they can generate audit trails and meet reporting requirements efficiently.
In this list, we offer the best self-hosted cloud-native open-source data warehouse solutions, for enterprise and business intellegence agencies.
1. RudderStack
RudderStack is a free and open-source privacy and security focused segment-alternative, in Golang and React.
Features
- Warehouse-first: RudderStack treats your data warehouse as a first class citizen among destinations, with advanced features and configurable, near real-time sync.
- Developer-focused: RudderStack is built API-first. It integrates seamlessly with the tools that the developers already use and love.
- High Availability: RudderStack comes with at least 99.99% uptime. We have built a sophisticated error handling and retry system that ensures that your data will be delivered even in the event of network partitions or destinations downtime.
- Privacy and Security: You can collect and store your customer data without sending everything to a third-party vendor. With RudderStack, you get fine-grained control over what data to forward to which analytical tool.
- Unlimited Events: Event volume-based pricing of most of the commercial systems is broken. With RudderStack Open Source, you can collect as much data as possible without worrying about overrunning your event budgets.
- Segment API-compatible: RudderStack is fully compatible with the Segment API. So you don't need to change your app if you are using Segment; just integrate the RudderStack SDKs into your app and your events will keep flowing to the destinations (including data warehouses) as before.
- Production-ready: Companies like Mattermost, IFTTT, Torpedo, Grofers, 1mg, Nana, OnceHub, and dozens of large companies use RudderStack for collecting their events.
- Seamless Integration: RudderStack currently supports integration with over 90 popular tool and warehouse destinations.
- User-specified Transformation: RudderStack offers a powerful JavaScript-based event transformation framework which lets you enhance or transform your event data by combining it with your other internal data. Furthermore, as RudderStack runs inside your cloud or on-premise environment, you can easily access your production data to join with the event data.
rudderlabs2. Materialize
Materialize is a cloud-native data warehouse purpose-built for operational workloads where an analytical data warehouse would be too slow, and a stream processor would be too complicated.
Using SQL and common tools in the wider data ecosystem, Materialize allows you to build real-time automation, engaging customer experiences, and interactive data products that drive value for your business while reducing the cost of data freshness.
MaterializeInc3. Elementary
Elementary is a dbt-native data observability solution for data and analytics engineers. Set up in minutes, gain immediate visibility, detect data issues, send actionable alerts, and understand impact and root cause. Elementary has two offerings: an open-source package and managed platform.
elementary-data4. Tensorbase
TensorBase hopes the open source not become a copy game. TensorBase has a clear-cut opposition to fork communities, repeat wheels, or hack traffics for so-called reputations (like Github stars). After thoughts, we decided to temporarily leave the general data warehousing field.
Features
- Out-of-the-box to play
- Lighting fast architectural performance in Rust
- Modern redesigned columnar storage
- Top performance network transport server
- ClickHouse compatible syntax
- Green installation with DBA-Free ops
- Reliability and high availability (WIP)
- Cluster (WIP)
- Cloud-Native Adaptation (WIP)
- Arrow dataLake (...)
tensorbase5. Hue
Hue is a mature SQL Assistant for querying Databases & Data Warehouses.
- 1000+ customers
- Top Fortune 500
use Hue to quickly answer questions via self-service querying and are executing 100s of 1000s of queries daily.
cloudera6. ScratchDB
Scratch Data is a wrapper that lets you stream data into and out of your analytics database. It takes arbitrary JSON as input and lets you perform analytical queries.
scratchdata7. Optimus
Optimus is an easy-to-use, reliable, and performant workflow orchestrator for data transformation, data modeling, pipelines, and data quality management.
It enables data analysts and engineers to transform their data by writing simple SQL queries and YAML configuration while Optimus handles dependency management, scheduling and all other aspects of running transformation jobs at scale.
Features
- Warehouse management: Optimus allows you to create and manage your data warehouse tables and views through YAML based configuration.
- Scheduling: Optimus provides an easy way to schedule your SQL transformation through a YAML based configuration.
- Automatic dependency resolution: Optimus parses your data transformation queries and builds a dependency graphs automaticaly instead of users defining their source and taget dependencies in DAGs.
- Dry runs: Before SQL query is scheduled for transformation, during deployment query will be dry-run to make sure it passes basic sanity checks.
- Powerful templating: Optimus provides query compile time templating with variables, loop, if statements, macros, etc for allowing users to write complex tranformation logic.
- Cross tenant dependency: Optimus is a multi-tenant service, if there are two tenants registered, serviceA and serviceB then service B can write queries referencing serviceA as source and Optimus will handle this dependency as well.
- Hooks: Optimus provides hooks for post tranformation logic. e,g. You can sink BigQuery tables to Kafka.
- Extensibility: Optimus support Python transformation and allows for writing custom plugins.
- Workflows: Optimus provides industry proven workflows using git based specification management and REST/GRPC based specification management for data warehouse management.
raystack8. Skytrax-Data-Warehouse
This is a full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.
iam-mhaseeb9. Real-time-Data-Warehouse
Real-time Data Warehouse with Apache Flink & Apache Kafka & Apache Hudi.
izhangzhihao10. Dinky
Dinky is a real-time data development platform based on Apache Flink, enabling agile data development, deployment and operation.
Features
- Immersive Flink SQL Data Development: Dinky provides prompt completion, statement beautification, online debugging, syntax verification, logic plan, catalog, lineage, version comparison, and more.
- Support FlinkSQL multi-version development and execution modes: Dinky supports multiple development and execution modes for FlinkSQL, including Local, Standalone, Yarn/Kubernetes Session, Yarn Per-Job, and Yarn/Kubernetes Application.
- Support Flink ecosystem: Connector, FlinkCEP, FlinkCDC, Paimon, PyFlink
- Support FlinkSQL syntax enhancement: Dinky enhances FlinkSQL with features like database synchronization, execution environments, global variables, table-valued aggregate functions, load dependency, row-level permissions, and execute jar.
- Support real-time warehousing and lake entry of the entire FlinkCDC database and FlinkCDC Pipeline task.
- Support real-time online debugging: Preview Table, ChangeLog and UDF.
- Support Flink Catalog, data source metadata online query and management.
- Support real-time task operation and maintenance: Online and offline, job information, job log, version info, job snapshot, monitor, sql lineage, alarm record, etc.
- Support real-time job alarm and alarm group: DingTalk, WeChat, Feishu, E-mail, SMS, Http etc.
- Support automatically managed SavePoint/CheckPoint recovery and triggering mechanisms: latest, earliest, specified, etc.
- Support resource management: Cluster instance, cluster configuration, data source, alarm, document, global variable, git project, UDF, resource, system configuration, etc.
- Support enterprise-level management: multi-tenant, user, role, token.
- More hidden features await exploration by our users.
DataLinkDC11. Multiwoven
Multiwoven is an open-source alternative to HighTouch, Census, and RudderStack. With Multiwoven, you can easily sync data from your data warehouse to any business tool, turning your data warehouse into a Customer Data Platform (CDP).
Multiwoven12. Dataplane
Dataplane is an Airflow inspired unified data platform with additional data mesh and RPA capability to automate, schedule and design data pipelines and workflows. Dataplane is written in Golang with a React front end.
Hazem Abbas
13. LakeFS
lakeFS is an open-source tool that transforms your object storage into a Git-like repository. It enables you to manage your data lake the way you manage your code.
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 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, DuckDB, and Presto.
treeverse14. Kylo
Kylo is an enterprise-ready modern data lake management software platform for big data engines such as Teradata, Apache Spark, or Hadoop. Kylo enforces best practices around metadata management, governance, and security gathered from experience in over 150+ successful big data projects.
TeradataHazem Abbas
