Alternatives to Trino

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.

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.

Apache DataFusion is a versatile and efficient query engine crafted in Rust, leveraging Apache Arrow for its in-memory data representation. It caters to developers engaged in creating data-focused systems, including databases, data frames, machine learning models, and real-time streaming applications. With its SQL and DataFrame APIs, DataFusion features a vectorized, multi-threaded execution engine that processes data streams efficiently and supports various partitioned data sources. It is compatible with several native formats such as CSV, Parquet, JSON, and Avro, and facilitates smooth integration with popular object storage solutions like AWS S3, Azure Blob Storage, and Google Cloud Storage. The architecture includes a robust query planner and an advanced optimizer that boasts capabilities such as expression coercion, simplification, and optimizations that consider distribution and sorting, along with automatic reordering of joins. Furthermore, DataFusion allows for extensive customization, enabling developers to incorporate user-defined scalar, aggregate, and window functions along with custom data sources and query languages, making it a powerful tool for diverse data processing needs. This adaptability ensures that developers can tailor the engine to fit their unique use cases effectively.

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.

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.

A SQL query engine that operates without a predefined schema, designed for use with Hadoop, NoSQL databases, and cloud storage solutions. This innovative engine allows for flexible data retrieval and analysis across various storage types, adapting seamlessly to diverse data structures.

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.

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.

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.

Apache Kylin™ is a distributed, open-source Analytical Data Warehouse designed for Big Data, aimed at delivering OLAP (Online Analytical Processing) capabilities in the modern big data landscape. By enhancing multi-dimensional cube technology and precalculation methods on platforms like Hadoop and Spark, Kylin maintains a consistent query performance, even as data volumes continue to expand. This innovation reduces query response times from several minutes to just milliseconds, effectively reintroducing online analytics into the realm of big data. Capable of processing over 10 billion rows in under a second, Kylin eliminates the delays previously associated with report generation, facilitating timely decision-making. It seamlessly integrates data stored on Hadoop with popular BI tools such as Tableau, PowerBI/Excel, MSTR, QlikSense, Hue, and SuperSet, significantly accelerating business intelligence operations on Hadoop. As a robust Analytical Data Warehouse, Kylin supports ANSI SQL queries on Hadoop/Spark and encompasses a wide array of ANSI SQL functions. Moreover, Kylin’s architecture allows it to handle thousands of simultaneous interactive queries with minimal resource usage, ensuring efficient analytics even under heavy loads. This efficiency positions Kylin as an essential tool for organizations seeking to leverage their data for strategic insights.

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.

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.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

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.

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.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

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.

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.

Starburst empowers organizations to enhance their decision-making capabilities by providing rapid access to all their data without the hassle of transferring or duplicating it. As companies accumulate vast amounts of data, their analysis teams often find themselves waiting for access to perform their evaluations. By facilitating direct access to data at its source, Starburst ensures that teams can quickly and accurately analyze larger datasets without the need for data movement. Starburst Enterprise offers a robust, enterprise-grade version of the open-source Trino (formerly known as Presto® SQL), which is fully supported and tested for production use. This solution not only boosts performance and security but also simplifies the deployment, connection, and management of a Trino environment. By enabling connections to any data source—be it on-premises, in the cloud, or within a hybrid cloud setup—Starburst allows teams to utilize their preferred analytics tools while seamlessly accessing data stored in various locations. This innovative approach significantly reduces the time taken for insights, helping businesses stay competitive in a data-driven world.

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.

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.

PySpark serves as the Python interface for Apache Spark, enabling the development of Spark applications through Python APIs and offering an interactive shell for data analysis in a distributed setting. In addition to facilitating Python-based development, PySpark encompasses a wide range of Spark functionalities, including Spark SQL, DataFrame support, Streaming capabilities, MLlib for machine learning, and the core features of Spark itself. Spark SQL, a dedicated module within Spark, specializes in structured data processing and introduces a programming abstraction known as DataFrame, functioning also as a distributed SQL query engine. Leveraging the capabilities of Spark, the streaming component allows for the execution of advanced interactive and analytical applications that can process both real-time and historical data, while maintaining the inherent advantages of Spark, such as user-friendliness and robust fault tolerance. Furthermore, PySpark's integration with these features empowers users to handle complex data operations efficiently across various datasets.

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.

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.

Qubole stands out as a straightforward, accessible, and secure Data Lake Platform tailored for machine learning, streaming, and ad-hoc analysis. Our comprehensive platform streamlines the execution of Data pipelines, Streaming Analytics, and Machine Learning tasks across any cloud environment, significantly minimizing both time and effort. No other solution matches the openness and versatility in handling data workloads that Qubole provides, all while achieving a reduction in cloud data lake expenses by more than 50 percent. By enabling quicker access to extensive petabytes of secure, reliable, and trustworthy datasets, we empower users to work with both structured and unstructured data for Analytics and Machine Learning purposes. Users can efficiently perform ETL processes, analytics, and AI/ML tasks in a seamless workflow, utilizing top-tier open-source engines along with a variety of formats, libraries, and programming languages tailored to their data's volume, diversity, service level agreements (SLAs), and organizational regulations. This adaptability ensures that Qubole remains a preferred choice for organizations aiming to optimize their data management strategies while leveraging the latest technological advancements.

Apache Hive is a data warehouse solution that enables the efficient reading, writing, and management of substantial datasets stored across distributed systems using SQL. It allows users to apply structure to pre-existing data in storage. To facilitate user access, it comes equipped with a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially part of the Apache® Hadoop® ecosystem, it has since evolved into an independent top-level project. We invite you to explore the project further and share your knowledge to enhance its development. Users typically implement traditional SQL queries through the MapReduce Java API, which can complicate the execution of SQL applications on distributed data. However, Hive simplifies this process by offering a SQL abstraction that allows for the integration of SQL-like queries, known as HiveQL, into the underlying Java framework, eliminating the need to delve into the complexities of the low-level Java API. This makes working with large datasets more accessible and efficient for developers.

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.

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.

Impala offers rapid response times and accommodates numerous concurrent users for business intelligence and analytical inquiries within the Hadoop ecosystem, supporting technologies such as Iceberg, various open data formats, and multiple cloud storage solutions. Additionally, it exhibits linear scalability, even when deployed in environments with multiple tenants. The platform seamlessly integrates with Hadoop's native security measures and employs Kerberos for user authentication, while the Ranger module provides a means to manage permissions, ensuring that only authorized users and applications can access specific data. You can leverage the same file formats, data types, metadata, and frameworks for security and resource management as those used in your Hadoop setup, avoiding unnecessary infrastructure and preventing data duplication or conversion. For users familiar with Apache Hive, Impala is compatible with the same metadata and ODBC driver, streamlining the transition. It also supports SQL, which eliminates the need to develop a new implementation from scratch. With Impala, a greater number of users can access and analyze a wider array of data through a unified repository, relying on metadata that tracks information right from the source to analysis. This unified approach enhances efficiency and optimizes data accessibility across various applications.

Handling and storing tabular data, such as that found in CSV or Parquet formats, is essential for data management. Transferring large result sets to clients is a common requirement, especially in extensive client/server frameworks designed for centralized enterprise data warehousing. Additionally, writing to a single database from various simultaneous processes poses its own set of challenges. DuckDB serves as a relational database management system (RDBMS), which is a specialized system for overseeing data organized into relations. In this context, a relation refers to a table, characterized by a named collection of rows. Each row within a table maintains a consistent structure of named columns, with each column designated to hold a specific data type. Furthermore, tables are organized within schemas, and a complete database comprises a collection of these schemas, providing structured access to the stored data. This organization not only enhances data integrity but also facilitates efficient querying and reporting across diverse datasets.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

Amazon Timestream is an efficient, scalable, and serverless time series database designed for IoT and operational applications, capable of storing and analyzing trillions of events daily with speeds up to 1,000 times faster and costs as low as 1/10th that of traditional relational databases. By efficiently managing the lifecycle of time series data, Amazon Timestream reduces both time and expenses by keeping current data in memory while systematically transferring historical data to a more cost-effective storage tier based on user-defined policies. Its specialized query engine allows users to seamlessly access and analyze both recent and historical data without the need to specify whether the data is in memory or in the cost-optimized tier. Additionally, Amazon Timestream features integrated time series analytics functions, enabling users to detect trends and patterns in their data almost in real-time, making it an invaluable tool for data-driven decision-making. Furthermore, this service is designed to scale effortlessly with your data needs while ensuring optimal performance and cost efficiency.

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.

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

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.

At the core of our innovative approach lies in-data computing, a cutting-edge technology aimed at efficiently processing substantial volumes of data. Our leading product, BigObject, is a prime example of this technology; it is a time series database purposefully created to enable rapid storage and management of vast data sets. Leveraging in-data computing, BigObject has the capability to swiftly and continuously address diverse data streams without interruption. This time series database excels in both high-speed storage and data analysis, showcasing remarkable performance alongside robust complex query functionalities. By transitioning from a traditional relational data structure to a time-series model, it harnesses in-data computing to enhance overall database efficiency. The foundation of our technology is an abstract model, wherein all data resides within an infinite and persistent memory space, facilitating seamless storage and computation. This unique architecture not only optimizes performance but also paves the way for future advancements in data processing capabilities.

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.

Scale from zero to millions of events per second effortlessly. Arroyo is delivered as a single, compact binary, allowing for local development on MacOS or Linux, and seamless deployment to production environments using Docker or Kubernetes. As a pioneering stream processing engine, Arroyo has been specifically designed to simplify real-time processing, making it more accessible than traditional batch processing. Its architecture empowers anyone with SQL knowledge to create dependable, efficient, and accurate streaming pipelines. Data scientists and engineers can independently develop comprehensive real-time applications, models, and dashboards without needing a specialized team of streaming professionals. By employing SQL, users can transform, filter, aggregate, and join data streams, all while achieving sub-second response times. Your streaming pipelines should remain stable and not trigger alerts simply because Kubernetes has chosen to reschedule your pods. Built for modern, elastic cloud infrastructures, Arroyo supports everything from straightforward container runtimes like Fargate to complex, distributed setups on Kubernetes, ensuring versatility and robust performance across various environments. This innovative approach to stream processing significantly enhances the ability to manage data flows in real-time applications.

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.

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.

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.

Dynamic and engaging visualizations enable the discovery of trends within user and business processes, offering comprehensive insight into the foundational computations. A concise collection of sequential operations delivers extensive functionality and meticulous control, all achievable in fewer than ten lines of code. An adaptive query engine allows users to effortlessly balance the trade-offs between query accuracy, processing speed, and costs to suit their specific requirements. Currently, Motif employs a specialized domain-specific language known as Sequence Operations Language (SOL), which we find to be more intuitive than SQL while providing greater capabilities than a simple drag-and-drop interface. Additionally, we have developed a bespoke engine designed to enhance the efficiency of sequence queries, while strategically sacrificing unnecessary precision that does not contribute to decision-making, in favor of improving query performance. This approach not only streamlines the user experience but also maximizes the effectiveness of data analysis.

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.