Best Data Warehouse Software for Apache Spark

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.

BigLake is a storage platform that unifies data warehouses, lakes and allows BigQuery and open-source frameworks such as Spark to access data with fine-grained control. BigLake offers accelerated query performance across multicloud storage and open formats like Apache Iceberg. You can store one copy of your data across all data warehouses and lakes. Multi-cloud governance and fine-grained access control for distributed data. Integration with open-source analytics tools, and open data formats is seamless. You can unlock analytics on distributed data no matter where it is stored. While choosing the best open-source or cloud-native analytics tools over a single copy, you can also access analytics on distributed data. Fine-grained access control for open source engines such as Apache Spark, Presto and Trino and open formats like Parquet. BigQuery supports performant queries on data lakes. Integrates with Dataplex for management at scale, including logical organization.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

The Stackable platform was built with flexibility and openness in mind. It offers a curated collection of open source data apps such as Apache Kafka Apache Druid Trino and Apache Spark. Stackable is different from other offerings that either push proprietary solutions or further vendor lock-in. All data apps are seamlessly integrated and can be added to or removed at any time. It runs anywhere, on-prem and in the cloud, based on Kubernetes. You only need stackablectl, a Kubernetes Cluster and stackablectl to run your stackable data platform. You will be able to work with your data within minutes. Configure your one line startup command here. Similar to kubectl stackablectl was designed to interface easily with the Stackable data Platform. Use the command-line utility to deploy and maintain stackable data apps in Kubernetes. You can create, delete and update components with stackablectl.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Data warehouse automation is now faster. Vaultspeed is based on the Data Vault 2.0 standard, a decade of data integration experience, and the Vaultspeed automation tool. All Data Vault 2.0 objects are supported and available for implementation. Generate quality code fast for all scenarios in a Data Vault 2.0 integration system. Vaultspeed can be integrated into your existing setup to maximize your investment in knowledge and tools. You will be in compliance with the Data Vault 2.0 standard. Scalefree is our constant partner. Data Vault 2.0 models are stripped down to the bare essentials so that they can be loaded using the same loading pattern (repeatable) and have the same database structure. Vaultspeed uses a template system that understands the object types and allows for easy-to-set configuration parameters.

Actian Avalanche, a fully managed hybrid cloud service for data warehouse, is designed from the ground up in order to deliver high performance across all dimensions (data volume, concurrent users, and query complexity) at a fraction the cost of other solutions. It is a hybrid platform that can be deployed both on-premises and on multiple clouds including AWS Azure, Google Cloud, and Azure. This allows you to migrate and offload data to the cloud at your pace. Actian Avalanche offers the best price-performance ratio in the industry without the need for optimization or DBA tuning. You can get substantially better performance at a fraction of the cost of other solutions or choose the same performance at a significantly lower price. Avalanche, for example, offers up to 6x the price-performance advantages over Snowflake according to GigaOm’s TPC-H industry benchmark and more than many other appliance vendors.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

The only fully-managed cloud data lakehouse that can ingest data from all of your sources in minutes, and support all of your query engines on a large scale. All for a fraction the cost. With the ease of fully managed pipelines, you can ingest data from databases and event streams in near-real-time. You can query your data using any engine and support all of your use cases, including BI, AI/ML, real-time analytics and AI/ML. Simple usage-based pricing allows you to cut your costs by up to 50% compared with cloud data warehouses and ETL software. With a fully-managed, highly optimized cloud service, you can deploy in minutes and without any engineering overhead. Unify all your data into a single source and eliminate the need for data to be copied between data lakes and warehouses. Apache Hudi, Apache Iceberg and Delta Lake all offer omnidirectional interoperability, allowing you to choose the best table format for your needs. Configure managed pipelines quickly for database CDC and stream ingestion.

Open, hybrid data lakes for AI and analytics can be used to put your data to use, wherever it is located. Connect your data in any format and from anywhere. Access it through a shared metadata layer. By matching the right workloads to the right query engines, you can optimize workloads in terms of price and performance. Integrate natural-language semantic searching without the need for SQL to unlock AI insights faster. Manage and prepare trusted datasets to improve the accuracy and relevance of your AI applications. Use all of your data everywhere. Watsonx.data offers the speed and flexibility of a warehouse, along with special features that support AI. This allows you to scale AI and analytics throughout your business. Choose the right engines to suit your workloads. You can manage your cost, performance and capability by choosing from a variety of open engines, including Presto C++ and Spark Milvus.

Apache Kylin™, an open-source distributed Analytical Data Warehouse (Big Data), was created to provide OLAP (Online Analytical Processing), in this big data era. Kylin can query at near constant speed regardless of increasing data volumes by renovating the multi-dimensional cube, precalculation technology on Hadoop or Spark, and thereby achieving almost constant query speed. Kylin reduces query latency from minutes down to a fraction of a second, bringing online analytics back into big data. Kylin can analyze more than 10+ billion rows in less time than a second. No more waiting for reports to make critical decisions. Kylin connects Hadoop data to BI tools such as Tableau, PowerBI/Excel and MSTR. This makes Hadoop BI faster than ever. Kylin is an Analytical Data Warehouse and offers ANSI SQL on Hadoop/Spark. It also supports most ANSI SQL queries functions. Because of the low resource consumption for each query, Kylin can support thousands upon thousands of interactive queries simultaneously.

Hudi is a rich platform for building streaming data lakes using incremental data pipelines on a self managing database layer. It can also be optimized for regular batch processing and lake engines. Hudi keeps a timeline of all actions on the table at different times. This allows for instantaneous views and efficient retrieval of data in the order they were received. The following components make up a Hudi instant. Hudi provides efficient upserts by mapping a given Hoodie key consistently with a file ID, via an indexing mechanism. Once a record is written to a file, the mapping between record key/file group/file ID never changes. The mapped file group includes all versions of a group record.

VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale. In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries. Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes. Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration. Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.

Palo helps enterprises create the PB level MPP architecture data warehouse services in just a few minutes and import massive data from RDS BOS and BMR. Palo is able to perform multi-dimensional analysis of big data. Palo is compatible to mainstream BI tools. Data analysts can quickly gain insights by analyzing and displaying the data visually. It has an industry-leading MPP engine with column storage, intelligent indexes, and vector execution functions. It can also provide advanced analytics, window functions and in-library analytics. You can create a materialized table and change its structure without suspending service. It supports flexible data recovery.

Archon Data Store™ is an open-source archive lakehouse platform that allows you to store, manage and gain insights from large volumes of data. Its minimal footprint and compliance features enable large-scale processing and analysis of structured and unstructured data within your organization. Archon Data Store combines data warehouses, data lakes and other features into a single platform. This unified approach eliminates silos of data, streamlining workflows in data engineering, analytics and data science. Archon Data Store ensures data integrity through metadata centralization, optimized storage, and distributed computing. Its common approach to managing data, securing it, and governing it helps you innovate faster and operate more efficiently. Archon Data Store is a single platform that archives and analyzes all of your organization's data, while providing operational efficiencies.