Alternatives to Apache Kylin

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

icCube, a Swiss-made analytics solution, is crafted for B2B SaaS product and development teams aiming to integrate advanced analytics directly into their applications. Our dashboards are designed to merge effortlessly within the SaaS solution's UI and UX, powered by icCube’s strong analytical engine, which supports complex data models with high-level security features. Adopting a developer-to-developer approach, the icCube team guides clients to ensure a smooth, rapid transition to production. We recognize the challenges of data navigation, so we’re thrilled to offer our Data Analytics Boutique Services. Tailored for both new and existing clients, this suite provides seamless data integration, fortified security, deep insights, automated decision-making, and visually impactful reports. At every project stage and throughout the product lifecycle, we partner closely with our clients, from providing quick feedback to full project and product launches.

Accelerate your data journey with AnalyticsCreator. Automate the design, development, and deployment of modern data architectures, including dimensional models, data marts, and data vaults or a combination of modeling techniques. Seamlessly integrate with leading platforms like Microsoft Fabric, Power BI, Snowflake, Tableau, and Azure Synapse and more. Experience streamlined development with automated documentation, lineage tracking, and schema evolution. Our intelligent metadata engine empowers rapid prototyping and deployment of analytics and data solutions. Reduce time-consuming manual tasks, allowing you to focus on data-driven insights and business outcomes. AnalyticsCreator supports agile methodologies and modern data engineering workflows, including CI/CD. Let AnalyticsCreator handle the complexities of data modeling and transformation, enabling you to unlock the full potential of your data

Snowflake is a cloud-native data platform that combines data warehousing, data lakes, and data sharing into a single solution. By offering elastic scalability and automatic scaling, Snowflake enables businesses to handle vast amounts of data while maintaining high performance at low cost. The platform's architecture allows users to separate storage and compute, offering flexibility in managing workloads. Snowflake supports real-time data sharing and integrates seamlessly with other analytics tools, enabling teams to collaborate and gain insights from their data more efficiently. Its secure, multi-cloud architecture makes it a strong choice for enterprises looking to leverage data at scale.

Amazon Redshift is the preferred choice among customers for cloud data warehousing, outpacing all competitors in popularity. It supports analytical tasks for a diverse range of organizations, from Fortune 500 companies to emerging startups, facilitating their evolution into large-scale enterprises, as evidenced by Lyft's growth. No other data warehouse simplifies the process of extracting insights from extensive datasets as effectively as Redshift. Users can perform queries on vast amounts of structured and semi-structured data across their operational databases, data lakes, and the data warehouse using standard SQL queries. Moreover, Redshift allows for the seamless saving of query results back to S3 data lakes in open formats like Apache Parquet, enabling further analysis through various analytics services, including Amazon EMR, Amazon Athena, and Amazon SageMaker. Recognized as the fastest cloud data warehouse globally, Redshift continues to enhance its performance year after year. For workloads that demand high performance, the new RA3 instances provide up to three times the performance compared to any other cloud data warehouse available today, ensuring businesses can operate at peak efficiency. This combination of speed and user-friendly features makes Redshift a compelling choice for organizations of all sizes.

When deployed as an on-premises server, SQL Server Analysis Services provides comprehensive support for various model types, including tabular models at all compatibility levels based on the version, multidimensional models, data mining capabilities, and Power Pivot features for SharePoint. The standard process for implementation involves setting up a SQL Server Analysis Services instance, designing either a tabular or multidimensional data model, deploying this model as a database to the server instance, processing it to populate with data, and configuring user permissions to facilitate data access. Once the setup is complete, client applications that are compatible with Analysis Services can easily utilize the data model as a source. These models typically gather data from external systems, primarily from data warehouses utilizing either SQL Server or Oracle relational database engines, though tabular models can connect to a variety of additional data sources. This versatility makes SQL Server Analysis Services a powerful tool for analytics and business intelligence.

Pinot is built to efficiently handle OLAP queries on static data with minimal latency. It incorporates various pluggable indexing methods, including Sorted Index, Bitmap Index, and Inverted Index. While it currently lacks support for joins, this limitation can be mitigated by utilizing Trino or PrestoDB for querying purposes. The system offers an SQL-like language that enables selection, aggregation, filtering, grouping, ordering, and distinct queries on datasets. It comprises both offline and real-time tables, with real-time tables being utilized to address segments lacking offline data. Additionally, users can tailor the anomaly detection process and notification mechanisms to accurately identify anomalies. This flexibility ensures that users can maintain data integrity and respond proactively to potential issues.

SelectDB is an innovative data warehouse built on Apache Doris, designed for swift query analysis on extensive real-time datasets. Transitioning from Clickhouse to Apache Doris facilitates the separation of the data lake and promotes an upgrade to a more efficient lake warehouse structure. This high-speed OLAP system handles nearly a billion query requests daily, catering to various data service needs across multiple scenarios. To address issues such as storage redundancy, resource contention, and the complexities of data governance and querying, the original lake warehouse architecture was restructured with Apache Doris. By leveraging Doris's capabilities for materialized view rewriting and automated services, it achieves both high-performance data querying and adaptable data governance strategies. The system allows for real-time data writing within seconds and enables the synchronization of streaming data from databases. With a storage engine that supports immediate updates and enhancements, it also facilitates real-time pre-polymerization of data for improved processing efficiency. This integration marks a significant advancement in the management and utilization of large-scale real-time data.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.

Azure Synapse represents the advanced evolution of Azure SQL Data Warehouse. It is a comprehensive analytics service that integrates enterprise data warehousing with Big Data analytics capabilities. Users can query data flexibly, choosing between serverless or provisioned resources, and can do so at scale. By merging these two domains, Azure Synapse offers a cohesive experience for ingesting, preparing, managing, and delivering data, catering to the immediate requirements of business intelligence and machine learning applications. This integration enhances the efficiency and effectiveness of data-driven decision-making processes.

Firebolt offers incredible speed and flexibility to tackle even the most daunting data challenges. By completely reimagining the cloud data warehouse, Firebolt provides an exceptionally rapid and efficient analytics experience regardless of scale. This significant leap in performance enables you to process larger datasets with greater detail through remarkably swift queries. You can effortlessly adjust your resources to accommodate any workload, volume of data, and number of simultaneous users. At Firebolt, we are committed to making data warehouses far more user-friendly than what has traditionally been available. This commitment drives us to simplify processes that were once complex and time-consuming into manageable tasks. Unlike other cloud data warehouse providers that profit from the resources you utilize, our model prioritizes transparency and fairness. We offer a pricing structure that ensures you can expand your operations without incurring excessive costs, making our solution not only efficient but also economical. Ultimately, Firebolt empowers organizations to harness the full potential of their data without the usual headaches.

The Unified Analytics Warehouse. The Unified Analytics Warehouse is the best place to find high-performing analytics and machine learning at large scale. Tech research analysts are seeing new leaders as they strive to deliver game-changing big data analytics. Vertica empowers data-driven companies so they can make the most of their analytics initiatives. It offers advanced time-series, geospatial, and machine learning capabilities, as well as data lake integration, user-definable extensions, cloud-optimized architecture and more. Vertica's Under the Hood webcast series allows you to dive into the features of Vertica - delivered by Vertica engineers, technical experts, and others - and discover what makes it the most scalable and scalable advanced analytical data database on the market. Vertica supports the most data-driven disruptors around the globe in their pursuit for industry and business transformation.

Databend is an innovative, cloud-native data warehouse crafted to provide high-performance and cost-effective analytics for extensive data processing needs. Its architecture is elastic, allowing it to scale dynamically in response to varying workload demands, thus promoting efficient resource use and reducing operational expenses. Developed in Rust, Databend delivers outstanding performance through features such as vectorized query execution and columnar storage, which significantly enhance data retrieval and processing efficiency. The cloud-first architecture facilitates smooth integration with various cloud platforms while prioritizing reliability, data consistency, and fault tolerance. As an open-source solution, Databend presents a versatile and accessible option for data teams aiming to manage big data analytics effectively in cloud environments. Additionally, its continuous updates and community support ensure that users can take advantage of the latest advancements in data processing technology.

GeoSpock revolutionizes data integration for a connected universe through its innovative GeoSpock DB, a cutting-edge space-time analytics database. This cloud-native solution is specifically designed for effective querying of real-world scenarios, enabling the combination of diverse Internet of Things (IoT) data sources to fully harness their potential, while also streamlining complexity and reducing expenses. With GeoSpock DB, users benefit from efficient data storage, seamless fusion, and quick programmatic access, allowing for the execution of ANSI SQL queries and the ability to link with analytics platforms through JDBC/ODBC connectors. Analysts can easily conduct evaluations and disseminate insights using familiar toolsets, with compatibility for popular business intelligence tools like Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as support for data science and machine learning frameworks such as Python Notebooks and Apache Spark. Furthermore, the database can be effortlessly integrated with internal systems and web services, ensuring compatibility with open-source and visualization libraries, including Kepler and Cesium.js, thus expanding its versatility in various applications. This comprehensive approach empowers organizations to make data-driven decisions efficiently and effectively.

ClickHouse is an efficient, open-source OLAP database management system designed for high-speed data processing. Its column-oriented architecture facilitates the creation of analytical reports through real-time SQL queries. In terms of performance, ClickHouse outshines similar column-oriented database systems currently on the market. It has the capability to handle hundreds of millions to over a billion rows, as well as tens of gigabytes of data, on a single server per second. By maximizing the use of available hardware, ClickHouse ensures rapid query execution. The peak processing capacity for individual queries can exceed 2 terabytes per second, considering only the utilized columns after decompression. In a distributed environment, read operations are automatically optimized across available replicas to minimize latency. Additionally, ClickHouse features multi-master asynchronous replication, enabling deployment across various data centers. Each node operates equally, effectively eliminating potential single points of failure and enhancing overall reliability. This robust architecture allows organizations to maintain high availability and performance even under heavy workloads.

SingleStore, previously known as MemSQL, is a highly scalable and distributed SQL database that can operate in any environment. It is designed to provide exceptional performance for both transactional and analytical tasks while utilizing well-known relational models. This database supports continuous data ingestion, enabling operational analytics critical for frontline business activities. With the capacity to handle millions of events each second, SingleStore ensures ACID transactions and allows for the simultaneous analysis of vast amounts of data across various formats, including relational SQL, JSON, geospatial, and full-text search. It excels in data ingestion performance at scale and incorporates built-in batch loading alongside real-time data pipelines. Leveraging ANSI SQL, SingleStore offers rapid query responses for both current and historical data, facilitating ad hoc analysis through business intelligence tools. Additionally, it empowers users to execute machine learning algorithms for immediate scoring and conduct geoanalytic queries in real-time, thereby enhancing decision-making processes. Furthermore, its versatility makes it a strong choice for organizations looking to derive insights from diverse data types efficiently.

IBM Db2 encompasses a suite of data management solutions, prominently featuring the Db2 relational database. These offerings incorporate AI-driven functionalities designed to streamline the management of both structured and unstructured data across various on-premises and multicloud settings. By simplifying data accessibility, the Db2 suite empowers businesses to leverage the advantages of AI effectively. Most components of the Db2 family are integrated within the IBM Cloud Pak® for Data platform, available either as additional features or as built-in data source services, ensuring that nearly all data is accessible across hybrid or multicloud frameworks to support AI-driven applications. You can easily unify your transactional data repositories and swiftly extract insights through intelligent, universal querying across diverse data sources. The multimodel functionality helps reduce expenses by removing the necessity for data replication and migration. Additionally, Db2 offers enhanced flexibility, allowing for deployment on any cloud service provider, which further optimizes operational agility and responsiveness. This versatility in deployment options ensures that businesses can adapt their data management strategies as their needs evolve.

VantageCloud by Teradata is a next-gen cloud analytics ecosystem built to unify disparate data sources, deliver real-time AI-powered insights, and drive enterprise innovation with unprecedented efficiency. The platform includes VantageCloud Lake, designed for elastic scalability and GPU-accelerated AI workloads, and VantageCloud Enterprise, which supports robust analytics capabilities across secure hybrid and multi-cloud deployments. It seamlessly integrates with leading cloud providers like AWS, Azure, and Google Cloud, and supports open table formats like Apache Iceberg for greater data flexibility. With built-in support for advanced analytics, workload management, and cross-functional collaboration, VantageCloud provides the agility and power modern enterprises need to accelerate digital transformation and optimize operational outcomes.

Imply is a cutting-edge analytics platform that leverages Apache Druid to manage extensive, high-performance OLAP (Online Analytical Processing) tasks in real-time. It excels at ingesting data instantly, delivering rapid query results, and enabling intricate analytical inquiries across vast datasets while maintaining low latency. This platform is specifically designed for enterprises that require engaging analytics, real-time dashboards, and data-centric decision-making on a large scale. Users benefit from an intuitive interface for exploring data, enhanced by features like multi-tenancy, detailed access controls, and operational insights. Its distributed architecture and ability to scale make Imply particularly advantageous for applications in streaming data analysis, business intelligence, and real-time monitoring across various sectors. Furthermore, its capabilities ensure that organizations can efficiently adapt to increasing data demands and quickly derive actionable insights from their data.

BigLake serves as a storage engine that merges the functionalities of data warehouses and lakes, allowing BigQuery and open-source frameworks like Spark to efficiently access data while enforcing detailed access controls. It enhances query performance across various multi-cloud storage systems and supports open formats, including Apache Iceberg. Users can maintain a single version of data, ensuring consistent features across both data warehouses and lakes. With its capacity for fine-grained access management and comprehensive governance over distributed data, BigLake seamlessly integrates with open-source analytics tools and embraces open data formats. This solution empowers users to conduct analytics on distributed data, regardless of its storage location or method, while selecting the most suitable analytics tools, whether they be open-source or cloud-native, all based on a singular data copy. Additionally, it offers fine-grained access control for open-source engines such as Apache Spark, Presto, and Trino, along with formats like Parquet. As a result, users can execute high-performing queries on data lakes driven by BigQuery. Furthermore, BigLake collaborates with Dataplex, facilitating scalable management and logical organization of data assets. This integration not only enhances operational efficiency but also simplifies the complexities of data governance in large-scale environments.

Greenplum Database® stands out as a sophisticated, comprehensive, and open-source data warehouse solution. It excels in providing swift and robust analytics on data volumes that reach petabyte scales. Designed specifically for big data analytics, Greenplum Database is driven by a highly advanced cost-based query optimizer that ensures exceptional performance for analytical queries on extensive data sets. This project operates under the Apache 2 license, and we extend our gratitude to all current contributors while inviting new ones to join our efforts. In the Greenplum Database community, every contribution is valued, regardless of its size, and we actively encourage diverse forms of involvement. This platform serves as an open-source, massively parallel data environment tailored for analytics, machine learning, and artificial intelligence applications. Users can swiftly develop and implement models aimed at tackling complex challenges in fields such as cybersecurity, predictive maintenance, risk management, and fraud detection, among others. Dive into the experience of a fully integrated, feature-rich open-source analytics platform that empowers innovation.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

The Ocient Hyperscale Data Warehouse revolutionizes data transformation and loading within seconds, allowing organizations to efficiently store and analyze larger datasets while executing queries on hyperscale data up to 50 times faster. In order to provide cutting-edge data analytics, Ocient has entirely rethought its data warehouse architecture, facilitating rapid and ongoing analysis of intricate, hyperscale datasets. By positioning storage close to compute resources to enhance performance on standard industry hardware, the Ocient Hyperscale Data Warehouse allows users to transform, stream, or load data directly, delivering results for previously unattainable queries in mere seconds. With its optimization for standard hardware, Ocient boasts query performance benchmarks that surpass competitors by as much as 50 times. This innovative data warehouse not only meets but exceeds the demands of next-generation analytics in critical areas where traditional solutions struggle, thereby empowering organizations to achieve greater insights from their data. Ultimately, the Ocient Hyperscale Data Warehouse stands out as a powerful tool in the evolving landscape of data analytics.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

Palo empowers businesses to swiftly establish a PB-level MPP architecture data warehouse service in just minutes while seamlessly importing vast amounts of data from sources like RDS, BOS, and BMR. This capability enables Palo to execute multi-dimensional big data analytics effectively. Additionally, it integrates smoothly with popular BI tools, allowing data analysts to visualize and interpret data swiftly, thereby facilitating informed decision-making. Featuring a top-tier MPP query engine, Palo utilizes column storage, intelligent indexing, and vector execution to enhance performance. Moreover, it offers in-library analytics, window functions, and a range of advanced analytical features. Users can create materialized views and modify table structures without interrupting services, showcasing its flexibility. Furthermore, Palo ensures efficient data recovery, making it a reliable solution for enterprises looking to optimize their data management processes.

An in-memory, column-oriented database combined with a Massively Parallel Processing (MPP) architecture enables the rapid querying of billions of records within mere seconds. The distribution of queries across all nodes in a cluster ensures linear scalability, accommodating a larger number of users and facilitating sophisticated analytics. The integration of MPP, in-memory capabilities, and columnar storage culminates in a database optimized for exceptional data analytics performance. With various deployment options available, including SaaS, cloud, on-premises, and hybrid solutions, data analysis can be performed in any environment. Automatic tuning of queries minimizes maintenance efforts and reduces operational overhead. Additionally, the seamless integration and efficiency of performance provide enhanced capabilities at a significantly lower cost compared to traditional infrastructure. Innovative in-memory query processing has empowered a social networking company to enhance its performance, handling an impressive volume of 10 billion data sets annually. This consolidated data repository, paired with a high-speed engine, accelerates crucial analytics, leading to better patient outcomes and improved financial results for the organization. As a result, businesses can leverage this technology to make quicker data-driven decisions, ultimately driving further success.

Presto serves as an open-source distributed SQL query engine designed for executing interactive analytic queries across data sources that can range in size from gigabytes to petabytes. It addresses the challenges faced by data engineers who often navigate multiple query languages and interfaces tied to isolated databases and storage systems. Presto stands out as a quick and dependable solution by offering a unified ANSI SQL interface for comprehensive data analytics and your open lakehouse. Relying on different engines for various workloads often leads to the necessity of re-platforming in the future. However, with Presto, you benefit from a singular, familiar ANSI SQL language and one engine for all your analytic needs, negating the need to transition to another lakehouse engine. Additionally, it efficiently accommodates both interactive and batch workloads, handling small to large datasets and scaling from just a few users to thousands. By providing a straightforward ANSI SQL interface for all your data residing in varied siloed systems, Presto effectively integrates your entire data ecosystem, fostering seamless collaboration and accessibility across platforms. Ultimately, this integration empowers organizations to make more informed decisions based on a comprehensive view of their data landscape.

Data Warehousing Without Limits As traditional systems like Netezza find it challenging to maintain their relevance, and cloud-exclusive solutions such as Snowflake face limitations due to dependence on virtual machines utilizing standard hardware, Yellowbrick breaks through barriers related to cost-effectiveness and adaptability in both on-premises and cloud settings. With Yellowbrick, users can achieve 100 times the performance they would expect, allowing thousands of individuals to execute ad hoc queries significantly faster—between 10 to 100 times more efficiently—than what legacy or cloud-only data warehouses can offer, even when working with petabytes of data. This platform supports simultaneous querying of both real-time and archived data, enhancing data accessibility. It provides the flexibility to deploy applications across various environments—whether on-premises or in multiple public clouds—ensuring consistent data performance without incurring data egress fees. Additionally, Yellowbrick helps organizations save millions through its cost-effective, fixed-price subscription model that offers budget predictability; the more queries executed, the lower the cost per query becomes, making it an economically savvy choice for extensive data needs. Ultimately, with Yellowbrick, businesses can optimize their data strategies while enjoying unparalleled performance and flexibility.

Regardless of whether your project involves a single table or numerous tables, StarRocks guarantees an impressive performance improvement of at least 300% when compared to other widely used solutions. With its comprehensive array of connectors, you can seamlessly ingest streaming data and capture information in real time, ensuring that you always have access to the latest insights. The query engine is tailored to suit your specific use cases, allowing for adaptable analytics without the need to relocate data or modify SQL queries. This provides an effortless way to scale your analytics capabilities as required. StarRocks not only facilitates a swift transition from data to actionable insights, but also stands out with its unmatched performance, offering a holistic OLAP solution that addresses the most prevalent data analytics requirements. Its advanced memory-and-disk-based caching framework is purpose-built to reduce I/O overhead associated with retrieving data from external storage, significantly enhancing query performance while maintaining efficiency. This unique combination of features ensures that users can maximize their data's potential without unnecessary delays.

Explore a diverse array of SQL features that allow you to build applications ranging from straightforward analytics to complex hybrid transactional and analytical processing. If you're eager to uncover insights from your data, striving for efficiency, or facing tight deadlines, MonetDB can deliver query results in just seconds or even faster. For those looking to leverage or modify their own code and requiring specialized functions, MonetDB provides hooks to integrate user-defined functions in SQL, Python, R, or C/C++. Become part of the vibrant MonetDB community that spans over 130 countries, including students, educators, researchers, startups, small businesses, and large corporations. Embrace the forefront of analytical database technology and ride the wave of innovation! Save time with MonetDB’s straightforward installation process, allowing you to quickly get your database management system operational. This accessibility ensures that users of all backgrounds can efficiently harness the power of data for their projects.

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

Make informed decisions by efficiently testing and modeling intricate business assumptions, whether in the cloud or on-premises. Oracle Essbase empowers organizations to swiftly extract insights from multidimensional datasets through what-if analyses and data visualization tools. Forecasting both company and departmental performance becomes a straightforward task, enabling the development and management of analytic applications that leverage business drivers to simulate various what-if scenarios. Users can oversee workflows for multiple scenarios all within a unified interface, simplifying submissions and approvals. The sandboxing features allow for rapid testing and evaluation of models, ensuring the best-suited model is chosen for production. Additionally, financial and business analysts benefit from over 100 ready-to-use mathematical functions that can be effortlessly implemented to generate new data insights. This comprehensive approach enhances the strategic capabilities of organizations, ultimately driving better performance outcomes.

Hologres is a hybrid serving and analytical processing system designed for the cloud that integrates effortlessly with the big data ecosystem. It enables users to analyze and manage petabyte-scale data with remarkable concurrency and minimal latency. With Hologres, you can leverage your business intelligence tools to conduct multidimensional data analysis and gain insights into your business operations in real-time. This platform addresses common issues faced by traditional real-time data warehousing solutions, such as data silos and redundancy. Hologres effectively fulfills the needs for data migration while facilitating the real-time analysis of extensive data volumes. It delivers responses to queries on petabyte-scale datasets in under a second, empowering users to explore their data dynamically. Additionally, it supports highly concurrent writes and queries, reaching speeds of up to 100 million transactions per second (TPS), ensuring that data is immediately available for querying after it’s written. This immediate access to data enhances the overall efficiency of business analytics.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

SAP HANA is an in-memory database designed to handle both transactional and analytical workloads using a single copy of data, regardless of type. It effectively dissolves the barriers between transactional and analytical processes within organizations, facilitating rapid decision-making whether deployed on-premises or in the cloud. This innovative database management system empowers users to create intelligent, real-time solutions, enabling swift decision-making from a unified data source. By incorporating advanced analytics, it enhances the capabilities of next-generation transaction processing. Organizations can build data solutions that capitalize on cloud-native attributes such as scalability, speed, and performance. With SAP HANA Cloud, businesses can access reliable, actionable information from one cohesive platform while ensuring robust security, privacy, and data anonymization, reflecting proven enterprise standards. In today's fast-paced environment, an intelligent enterprise relies on timely insights derived from data, emphasizing the need for real-time delivery of such valuable information. As the demand for immediate access to insights grows, leveraging an efficient database like SAP HANA becomes increasingly critical for organizations aiming to stay competitive.

Leverage your data, regardless of its location, with an open and hybrid data lakehouse designed specifically for AI and analytics. Seamlessly integrate data from various sources and formats, all accessible through a unified entry point featuring a shared metadata layer. Enhance both cost efficiency and performance by aligning specific workloads with the most suitable query engines. Accelerate the discovery of generative AI insights with integrated natural-language semantic search, eliminating the need for SQL queries. Ensure that your AI applications are built on trusted data to enhance their relevance and accuracy. Maximize the potential of all your data, wherever it exists. Combining the rapidity of a data warehouse with the adaptability of a data lake, watsonx.data is engineered to facilitate the expansion of AI and analytics capabilities throughout your organization. Select the most appropriate engines tailored to your workloads to optimize your strategy. Enjoy the flexibility to manage expenses, performance, and features with access to an array of open engines, such as Presto, Presto C++, Spark Milvus, and many others, ensuring that your tools align perfectly with your data needs. This comprehensive approach allows for innovative solutions that can drive your business forward.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

biGENIUS automates all phases of analytic data management solutions (e.g. data warehouses, data lakes and data marts. thereby allowing you to turn your data into a business as quickly and cost-effectively as possible. Your data analytics solutions will save you time, effort and money. Easy integration of new ideas and data into data analytics solutions. The metadata-driven approach allows you to take advantage of new technologies. Advancement of digitalization requires traditional data warehouses (DWH) as well as business intelligence systems to harness an increasing amount of data. Analytical data management is essential to support business decision making today. It must integrate new data sources, support new technologies, and deliver effective solutions faster than ever, ideally with limited resources.

Introducing a unique cloud data lakehouse that is entirely managed and capable of ingesting data from all your sources within minutes, while seamlessly accommodating every query engine at scale, all at a significantly reduced cost. This platform enables ingestion from both databases and event streams at terabyte scale in near real-time, offering the ease of fully managed pipelines. Furthermore, you can execute queries using any engine, catering to diverse needs such as business intelligence, real-time analytics, and AI/ML applications. By adopting this solution, you can reduce your expenses by over 50% compared to traditional cloud data warehouses and ETL tools, thanks to straightforward usage-based pricing. Deployment is swift, taking just minutes, without the burden of engineering overhead, thanks to a fully managed and highly optimized cloud service. Consolidate your data into a single source of truth, eliminating the necessity of duplicating data across various warehouses and lakes. Select the appropriate table format for each task, benefitting from seamless interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Additionally, quickly set up managed pipelines for change data capture (CDC) and streaming ingestion, ensuring that your data architecture is both agile and efficient. This innovative approach not only streamlines your data processes but also enhances decision-making capabilities across your organization.

Hydra is an innovative, open-source solution that transforms Postgres into a column-oriented database, enabling instant queries over billions of rows without necessitating any alterations to your existing code. By employing advanced techniques such as parallelization and vectorization for aggregate functions like COUNT, SUM, and AVG, Hydra significantly enhances the speed and efficiency of data processing in Postgres. In just five minutes, you can set up Hydra without modifying your syntax, tools, data model, or extensions, ensuring a hassle-free integration. For those seeking a fully managed experience, Hydra Cloud offers seamless operations and optimal performance. Various industries can benefit from tailored analytics by leveraging powerful Postgres extensions and custom functions, allowing you to take charge of your data needs. Designed with user requirements in mind, Hydra stands out as the fastest Postgres solution available for analytical tasks, making it an essential tool for data-driven decision-making. With features like columnar storage, query parallelization, and vectorization, Hydra is poised to redefine the analytics landscape.

Qlik Compose for Data Warehouses offers a contemporary solution that streamlines and enhances the process of establishing and managing data warehouses. This tool not only automates the design of the warehouse but also generates ETL code and implements updates swiftly, all while adhering to established best practices and reliable design frameworks. By utilizing Qlik Compose for Data Warehouses, organizations can significantly cut down on the time, expense, and risk associated with BI initiatives, regardless of whether they are deployed on-premises or in the cloud. On the other hand, Qlik Compose for Data Lakes simplifies the creation of analytics-ready datasets by automating data pipeline processes. By handling data ingestion, schema setup, and ongoing updates, companies can achieve a quicker return on investment from their data lake resources, further enhancing their data strategy. Ultimately, these tools empower organizations to maximize their data potential efficiently.

QuestDB is an advanced relational database that focuses on column-oriented storage optimized for time series and event-driven data. It incorporates SQL with additional features tailored for time-based analytics to facilitate real-time data processing. This documentation encompasses essential aspects of QuestDB, including initial setup instructions, comprehensive usage manuals, and reference materials for syntax, APIs, and configuration settings. Furthermore, it elaborates on the underlying architecture of QuestDB, outlining its methods for storing and querying data, while also highlighting unique functionalities and advantages offered by the platform. A key feature is the designated timestamp, which empowers time-focused queries and efficient data partitioning. Additionally, the symbol type enhances the efficiency of managing and retrieving frequently used strings. The storage model explains how QuestDB organizes records and partitions within its tables, and the use of indexes can significantly accelerate read access for specific columns. Moreover, partitions provide substantial performance improvements for both calculations and queries. With its SQL extensions, users can achieve high-performance time series analysis using a streamlined syntax that simplifies complex operations. Overall, QuestDB stands out as a powerful tool for handling time-oriented data effectively.

Infobright DB is an enterprise-grade database that utilizes a columnar storage architecture, enabling business analysts to efficiently analyze data and rapidly generate reports. This versatile database can be implemented both on-premise and in cloud environments. It is designed to store and analyze substantial amounts of big data, facilitating interactive business intelligence and handling complex queries with ease. By enhancing query performance and lowering storage costs, it significantly boosts overall efficiency in analytics and reporting processes. With capabilities to manage hundreds of terabytes of data, Infobright DB overcomes the limitations often faced by traditional databases. This solution supports big data applications while removing the need for indexing and partitioning, resulting in no administrative burden. In an era where machine data is growing exponentially, IgniteTech’s Infobright DB is purpose-built to deliver exceptional performance for large quantities of machine-generated information. Furthermore, it allows users to manage intricate ad hoc analytical environments without the heavy database administration demands seen in other solutions. This makes it an invaluable tool for organizations seeking to optimize their data handling and analysis.

Citus enhances the beloved Postgres experience by integrating the capability of distributed tables, while remaining fully open source. It now supports both schema-based and row-based sharding, alongside compatibility with Postgres 16. You can scale Postgres effectively by distributing both data and queries, starting with a single Citus node and seamlessly adding more nodes and rebalancing shards as your needs expand. By utilizing parallelism, maintaining a larger dataset in memory, increasing I/O bandwidth, and employing columnar compression, you can significantly accelerate query performance by up to 300 times or even higher. As an extension rather than a fork, Citus works with the latest versions of Postgres, allowing you to utilize your existing SQL tools and build on your Postgres knowledge. Additionally, you can alleviate infrastructure challenges by managing both transactional and analytical tasks within a single database system. Citus is available for free download as open source, giving you the option to self-manage it while actively contributing to its development through GitHub. Shift your focus from database concerns to application development by running your applications on Citus within the Azure Cosmos DB for PostgreSQL environment, making your workflow more efficient.

Designed specifically for optimizing compute, memory, and storage, Oxla serves as a self-hosted data warehouse that excels in handling large-scale, low-latency analytics while providing strong support for time-series data. While cloud data warehouses may suit many, they are not universally applicable; as operations expand, the ongoing costs of cloud computing can surpass initial savings on infrastructure, particularly in regulated sectors that demand comprehensive data control beyond mere VPC and BYOC setups. Oxla surpasses both traditional and cloud-based warehouses by maximizing efficiency, allowing for the scalability of expanding datasets with predictable expenses, whether on-premises or in various cloud environments. Deployment, execution, and maintenance of Oxla can be easily managed using Docker and YAML, enabling a range of workloads to thrive within a singular, self-hosted data warehouse. In this way, Oxla provides a tailored solution for organizations seeking both efficiency and control in their data management strategies.

ScyllaDB serves as an ideal database solution for applications that demand high performance and minimal latency, catering specifically to data-intensive needs. It empowers teams to fully utilize the growing computing capabilities of modern infrastructures, effectively removing obstacles to scaling as data volumes expand. Distinct from other database systems, ScyllaDB stands out as a distributed NoSQL database that is completely compatible with both Apache Cassandra and Amazon DynamoDB, while incorporating significant architectural innovations that deliver outstanding user experiences at significantly reduced costs. Over 400 transformative companies, including Disney+ Hotstar, Expedia, FireEye, Discord, Zillow, Starbucks, Comcast, and Samsung, rely on ScyllaDB to tackle their most challenging database requirements. Furthermore, ScyllaDB is offered in various formats, including a free open-source version, a fully-supported enterprise solution, and a fully managed database-as-a-service (DBaaS) available across multiple cloud platforms, ensuring flexibility for diverse user needs. This versatility makes it an attractive choice for organizations looking to optimize their database performance.

AlloyDB is a fully managed database service that is compatible with PostgreSQL, designed to meet the needs of the most demanding enterprise workloads. By merging Google's advancements with PostgreSQL, AlloyDB offers enhanced performance, scalability, and reliability. It ensures complete compatibility with PostgreSQL, allowing for both flexibility and genuine workload portability. For transactional workloads, its performance is up to four times faster than standard PostgreSQL, while it provides real-time analytical insights that are up to 100 times quicker. Additionally, AlloyDB AI supports the development of various generative AI applications. For versatile deployment, AlloyDB Omni is available as a downloadable version that can function in any environment. You can easily scale your resources and enjoy predictable performance, backed by a high availability service level agreement of 99.99%, which includes maintenance for the most intense enterprise demands. The automated systems, enhanced with machine learning capabilities, streamline management tasks such as database patching, backups, scaling, and replication, freeing users to focus on other priorities and innovations. This comprehensive approach makes AlloyDB a robust choice for organizations looking to optimize their database solutions.