Alternatives to StarRocks

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

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.

SAP HANA Cloud (DBaaS) is a fully managed, in-memory cloud database. It is the cloud-based data foundation of SAP Business Technology Platform. It integrates data from across enterprises, enabling faster decisions based upon live data. Modern architectures allow you to build data solutions and gain real-time insights that are business-ready. The SAP HANA Cloud database is the data foundation of SAP Business Technology Platform. It offers the power and flexibility of SAP HANA in a cloud environment. Scale to meet your business needs, process all types of business data, and perform advanced analytics on live transactions for faster, better decision-making. Native integration allows you to connect to distributed data, develop tools and applications across clouds and on-premise, as well as store and manage volatile data. You can tap business-ready information by creating a single source of truth. This will enable security, privacy and anonymization with enterprise reliability.

Amazon Redshift is preferred by more customers than any other cloud data storage. Redshift powers analytic workloads for Fortune 500 companies and startups, as well as everything in between. Redshift has helped Lyft grow from a startup to multi-billion-dollar enterprises. It's easier than any other data warehouse to gain new insights from all of your data. Redshift allows you to query petabytes (or more) of structured and semi-structured information across your operational database, data warehouse, and data lake using standard SQL. Redshift allows you to save your queries to your S3 database using open formats such as Apache Parquet. This allows you to further analyze other analytics services like Amazon EMR and Amazon Athena. Redshift is the fastest cloud data warehouse in the world and it gets faster each year. The new RA3 instances can be used for performance-intensive workloads to achieve up to 3x the performance compared to any cloud data warehouse.

Databend is an agile, cloud-native, modern data warehouse that delivers high-performance analytics at a low cost for large-scale data processing. It has an elastic architecture which scales dynamically in order to meet the needs of different workloads. This ensures efficient resource utilization and lower operating costs. Databend, written in Rust offers exceptional performance thanks to features such as vectorized query execution, columnar storage and optimized data retrieval and processing speed. Its cloud-first approach allows for seamless integration with cloud platforms and emphasizes reliability, consistency of data, and fault tolerance. Databend is a free and open-source solution that makes it an accessible and flexible choice for data teams who want to handle big data analysis in the cloud.

SelectDB is an advanced data warehouse built on Apache Doris. It supports rapid query analysis of large-scale, real-time data. Clickhouse to Apache Doris to separate the lake warehouse, and upgrade the lake storage. Fast-hand OLAP system carries out nearly 1 billion queries every day in order to provide data services for various scenes. The original lake warehouse separation was abandoned due to problems with storage redundancy and resource seizure. Also, it was difficult to query and adjust. It was decided to use Apache Doris lakewarehouse, along with Doris's materialized views rewriting capability and automated services to achieve high-performance query and flexible governance. Write real-time data within seconds and synchronize data from databases and streams. Data storage engine with real-time update and addition, as well as real-time polymerization.

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

Imply is a real time analytics platform built on Apache Druid. It was designed to handle large scale, high performance OLAP (Online Analytical Processing). It provides real-time data ingestion and fast query performance. It also allows for complex analytical queries to be performed on massive datasets at low latency. Imply is designed for organizations who need interactive analytics, real time dashboards, and data driven decision-making. It offers a user-friendly data exploration interface, as well as advanced features like multi-tenancy and fine-grained controls for access. Imply's distributed architecture and scalability make it ideal for use cases such as streaming data analytics, real-time monitoring, and business intelligence.

Oxla is a new-generation Online Analytical Process (OLAP) Database engineered for high-speed processing and efficiency. Its all-in one architecture allows rapid deployment without external dependencies and allows users to insert data and query it seamlessly. Oxla is compatible both with the PostgreSQL SQL dialect and wire protocol, making it easy to integrate with existing tools and workflows. The platform excels at both real-time processing as well as handling large, complex query, making it ideal for diverse analytical tasks. Oxla's design is optimized for modern hardware, including multi-core architectural capabilities, delivering superior performance to traditional analytical databases. It offers flexible deployment, including self hosted and cloud-based options, and provides a 1-core license that grants access to core functionality. Oxla's pay as you go pricing model ensures cost effectiveness, allowing users only to pay for the resources that they use.

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

Apache Druid, an open-source distributed data store, is Apache Druid. Druid's core design blends ideas from data warehouses and timeseries databases to create a high-performance real-time analytics database that can be used for a wide range of purposes. Druid combines key characteristics from each of these systems into its ingestion, storage format, querying, and core architecture. Druid compresses and stores each column separately, so it only needs to read the ones that are needed for a specific query. This allows for fast scans, ranking, groupBys, and groupBys. Druid creates indexes that are inverted for string values to allow for fast search and filter. Connectors out-of-the box for Apache Kafka and HDFS, AWS S3, stream processors, and many more. Druid intelligently divides data based upon time. Time-based queries are much faster than traditional databases. Druid automatically balances servers as you add or remove servers. Fault-tolerant architecture allows for server failures to be avoided.

SingleStore (formerly MemSQL), is a distributed, highly-scalable SQL Database that can be run anywhere. With familiar relational models, we deliver the best performance for both transactional and analytical workloads. SingleStore is a scalable SQL database which continuously ingests data to perform operational analysis for your business' front lines. ACID transactions allow you to simultaneously process millions of events per second and analyze billions of rows in relational SQL, JSON geospatial, full-text search, and other formats. SingleStore provides the best data ingestion performance and supports batch loading and real-time data pipelines. SingleStore allows you to query live and historical data with ANSI SQL in a lightning fast manner. You can perform ad-hoc analysis using business intelligence tools, run machine-learning algorithms for real time scoring, and geoanalytic queries in a real time.

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.

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

Pinot is designed to answer OLAP questions with low latency and immutable data. Pluggable indexing technologies: Sorted Index (Bitmap Index), Inverted Index. Trino and PrestoDB are both available for querying, but joins are not currently supported. SQL-like language that supports selection and aggregation, filtering as well as group by, order, and distinct queries on data. Both an offline and a real-time table are possible. Only use real-time table to cover segments where offline data is not yet available. Customize anomaly detection flow and notification flow to detect the right anomalies.

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

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

Timeplus is an easy-to-use, powerful and cost-effective platform for stream processing. All in one binary, easily deployable anywhere. We help data teams in organizations of any size and industry process streaming data and historical data quickly, intuitively and efficiently. Lightweight, one binary, no dependencies. Streaming analytics and historical functionality from end-to-end. 1/10 of the cost of comparable open source frameworks Transform real-time data from the market and transactions into real-time insight. Monitor financial data using append-only streams or key-value streams. Implement real-time feature pipelines using Timeplus. All infrastructure logs, metrics and traces are consolidated on one platform. In Timeplus we support a variety of data sources through our web console UI. You can also push data using REST API or create external streams, without copying data to Timeplus.

Processing and storage of tabular datasets, e.g. CSV or Parquet files. Large result set transfer to client. Large client/server installations are required for central enterprise data warehousing. Multiple concurrent processes can be used to write to a single database. DuckDB is a relational database management software (RDBMS). It is a system to manage data stored in relational databases. A relation is basically a mathematical term for a particular table. Each table is a named collection. Each row in a table has the same number of named columns. Each column is of a particular data type. Schemas are used to store tables, and a collection can be accessed to access the entire database.

Your cloud data platform. Access to any data you need with unlimited scalability. All your data is available to you, with the near-infinite performance and concurrency required by your organization. You can seamlessly share and consume shared data across your organization to collaborate and solve your most difficult business problems. You can increase productivity and reduce time to value by collaborating with data professionals to quickly deliver integrated data solutions from any location in your organization. Our technology partners and system integrators can help you deploy Snowflake to your success, no matter if you are moving data into Snowflake.

Scale from 0 to millions of events every second. Arroyo is shipped as a single compact binary. Run locally on MacOS, Linux or Kubernetes for development and deploy to production using Docker or Kubernetes. Arroyo is an entirely new stream processing engine that was built from the ground-up to make real time easier than batch. Arroyo has been designed so that anyone with SQL knowledge can build reliable, efficient and correct streaming pipelines. Data scientists and engineers are able to build real-time dashboards, models, and applications from end-to-end without the need for a separate streaming expert team. SQL allows you to transform, filter, aggregate and join data streams with results that are sub-second. Your streaming pipelines should not page someone because Kubernetes rescheduled your pods. Arroyo can run in a modern, elastic cloud environment, from simple container runtimes such as Fargate, to large, distributed deployments using the Kubernetes logo.

Presto is an open-source distributed SQL query engine that allows interactive analytic queries against any data source, from gigabytes up to petabytes.

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

A cloud database that can scale to handle large streaming data sets. Kinetica harnesses modern vectorized processors to perform orders of magnitude faster for real-time spatial or temporal workloads. In real-time, track and gain intelligence from billions upon billions of moving objects. Vectorization unlocks new levels in performance for analytics on spatial or time series data at large scale. You can query and ingest simultaneously to take action on real-time events. Kinetica's lockless architecture allows for distributed ingestion, which means data is always available to be accessed as soon as it arrives. Vectorized processing allows you to do more with fewer resources. More power means simpler data structures which can be stored more efficiently, which in turn allows you to spend less time engineering your data. Vectorized processing allows for incredibly fast analytics and detailed visualizations of moving objects at large scale.

InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University.

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

Now that your data has been in motion, it is time to make sense. Stream processing allows you to extract instant insights from your data streams but it can be difficult to set up the infrastructure. Confluent created ksqlDB to support stream processing applications. Continuously processing streams of data from your business will make your data actionable. The intuitive syntax of ksqlDB allows you to quickly access and augment Kafka data, allowing development teams to create innovative customer experiences and meet data-driven operational requirements. ksqlDB is a single solution that allows you to collect streams of data, enrich them and then serve queries on new derived streams or tables. This means that there is less infrastructure to manage, scale, secure, and deploy. You can now focus on the important things -- innovation -- with fewer moving parts in your data architecture.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.

You can query billions upon billions of rows with an in-memory columnar database and MPP architecture. Queries are distributed across all cluster nodes, allowing for linear scaling and advanced analytics. The fastest database for data analytics is made up of MPP, columnar storage, and in-memory. You can analyze data anywhere it is stored, whether you are using SaaS, cloud, hybrid, or on-premises deployments. Automatic query tuning reduces overhead and maintenance. You get more power for a fraction of the normal infrastructure costs with seamless integrations and performance efficiency. This social networking company was able to increase its performance by using smart, in-memory query processing. They processed 10B data sets per year. A single data repository and speed-engine to accelerate critical analytics, improving patient outcomes and the bottom line.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

Hydra is a column-oriented Postgres that is open source. No code changes required to query billions of rows. Hydra parallelizes, vectorizes, and aggregates (COUNTS, SUMs, AVGs) to deliver the speed that you've always desired on Postgres. Boost performance in every size! Hydra can be installed in just 5 minutes, without requiring any changes to your tools, extensions, syntax, data model or data model. Hydra Cloud allows for smooth sailing and fully managed operations. Different industries have different requirements. Take control of your analytics with powerful Postgres custom functions and extensions. Built by you for you. Hydra is the fastest Postgres on the market. Boost performance by using columnar storage, query parallelization, and vectorization.

QuestDB is a relational database that uses column-oriented databases. It can be used for event and time series data. It uses SQL with extensions to time series to aid in real-time analytics. These pages provide information about core concepts of QuestDB. They include setup steps, usage guides, as well as reference documentation for syntax, APIs, and configuration. This section explains the architecture of QuestDB and how it stores and queries data. It also introduces new capabilities and features that are unique to the system. The core feature of QuestDB is the designated timestamp. It enables partitioning and time-oriented language capabilities. The symbol type makes it easy to store and retrieve repetitive strings. QuestDB's storage model describes how it stores records and partitions within tables. Indexes can be used to provide faster access to specific columns. Partitions can be used to provide significant performance improvements in calculations and queries. SQL extensions enable time series analysis that is efficient and concise with a concise syntax.

SAP HANA is an in-memory database with high performance that accelerates data-driven decision-making and actions. It supports all workloads and provides the most advanced analytics on multi-model data on premise and in cloud.

Citus combines the Postgres you know and love with the power of distributed tables. 100% open source. Now with schema-based, row-based, and Postgres 16 support. Scale Postgres using data and queries. You can start off with a single Citus server, then add more nodes and rebalance the shards as you grow. Parallelism, storing more data in memory and using higher I/O bandwidth along with columnar compression can speed up queries by 20x or 300x. Citus is a new extension (not fork) of the latest Postgres version, allowing you to use your familiar SQL toolkit and leverage your Postgres expertise. Use a single database to manage both your analytical and transactional workloads. Download and use Citus Open Source for free. Citus can be managed by you, if you embrace open source and use GitHub. Focus on your app & forget your database. Azure Cosmos DB PostgreSQL for Citus allows you to run your app in the cloud.

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.

Easy cloud and on-premises testing of complex business assumptions allows you to make better decisions. Oracle Essbase empowers organizations to quickly generate insights from multidimensional data sets with what-if analysis and data visualization tools. Forecast company and departmental performance quickly and easily Business drivers are used to create and manage analytic apps. They can be used to model multiple what-if scenarios. You can manage workflows for multiple scenarios from one user interface. This allows for central submissions and approvals. Sandboxing allows you to quickly test and evaluate your models in order to determine which model is best for production. More than 100 pre-built mathematical functions are available for financial and business analysts that can be used to quickly derive new data.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

You can create flat or relational databases with unlimited fields, tables, and rows. A custom report builder is included. Create custom reports by connecting to compatible ODBC databases. You can create your own databases. Here are some highlights: Filter tables instantly - Ultra simple graphical-user-interface - One-click table and data form creation - You can open up to 5 databases simultaneously Export your data to comma-separated files - Create custom reports to all your databases - A complete helpfile for creating database reports - You can print tables and queries directly from your data grid - Supports any SQL standard your ODBC compatible databases require For best performance and user experience, please install and run this database app with full administrator rights. Requirements: . 1024x768 Display Size . Windows 98 / XP / Windows 8 / Windows 10 - 32bit or 64bit No Java or DotNet are required. Green Energy Software. One step at a time, saving the planet

QuasarDB is Quasar's brain. It is a high-performance distributed, column-oriented, timeseries database management software system that delivers real-time data for petascale use cases. You can save up to 20X on your disk usage Quasardb compression and ingestion are unmatched. Feature extraction can be performed up to 10,000 times faster. QuasarDB is able to extract features from raw data in real-time thanks to a combination of a builtin map/reduce engine, an aggregate engine that leverages SIMD from modern processors, and stochastic indices that consume virtually no disk space.

Amazon Timestream is a fast, scalable and serverless time series data service for IoT/operational applications. It makes it possible to store and analyze trillions per day up to 1000 times faster than traditional relational databases and at as low as 1/10th of the cost. Amazon Timestream helps you save time and money when managing the lifecycles of time series data. It stores recent data in memory and moves historical data to a cost-optimized storage tier according to user defined policies. Amazon Timestream's purpose-built query tool allows you to access and analyze both recent and historic data simultaneously, without having to specify in the query whether the data is in the in-memory tier or the cost-optimized. Amazon Timestream's built-in time series analytics functions allow you to identify trends and patterns within your data in real-time.

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

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Azure Synapse is the Azure SQL Data Warehouse. Azure Synapse, a limitless analytics platform that combines enterprise data warehouse and Big Data analytics, is called Azure Synapse. It allows you to query data at your own pace, with either serverless or provisioned resources - at scale. Azure Synapse combines these two worlds with a single experience to ingest and prepare, manage and serve data for machine learning and BI needs.

Open source solutions that require no maintenance can save you time and money. Your engineers will enjoy working with data because it is integrated, managed and highly reliable. DoubleCloud offers a range of managed open-source services, or you can leverage the full platform's power, including data storage and visualization, orchestration, ELT and real-time visualisation. We offer leading open-source solutions like ClickHouse Kafka and Airflow with deployments on Amazon Web Services and Google Cloud. Our no-code ELT allows real-time data sync between systems. It is fast, serverless and seamlessly integrated into your existing infrastructure. Our managed open-source data visualisation allows you to visualize your data in real time by creating charts and dashboards. Our platform is designed to make engineers' lives easier.

Greenplum Database®, an open-source data warehouse, is a fully featured, advanced, and fully functional data warehouse. It offers powerful and fast analytics on petabyte-scale data volumes. Greenplum Database is uniquely designed for big data analytics. It is powered by the most advanced cost-based query optimizer in the world, delivering high analytical query performance with large data volumes. The Apache 2 license is used to release Greenplum Database®. We would like to thank all of our community contributors. We are also open to new contributions. We encourage all contributions to the Greenplum Database community, no matter how small. Open-source, massively parallel data platform for machine learning, analytics, and AI. Rapidly create and deploy models to support complex applications in cybersecurity, predictive management, risk management, fraud detection, among other areas. The fully integrated, open-source analytics platform is now available.

InfobrightDB is a high performance enterprise database that leverages a columnar storage engine to allow business analysts to quickly and efficiently dissect data and obtain reports. InfoBrightDB can be deployed either on-premises or in the cloud.

A fully managed PostgreSQL-compatible database service for your most demanding enterprise workloads. AlloyDB combines PostgreSQL and Google's best features for performance, scale, availability, and superior performance. PostgreSQL is fully compatible, allowing you to move your workloads anywhere. Transactional workloads are 4x faster with PostgreSQL. Fast real-time insights and up to 100x faster analytical query speeds than standard PostgreSQL. AlloyDB AI helps you create a variety of AI applications. AlloyDB Omni, a downloadable version of AlloyDB, is designed to run anywhere. Scale up to achieve predictable performance, a high availability SLA (including maintenance) of 99.99% for your most demanding enterprise workloads. Automated and machine-learning-enabled autopilots simplify management by handling database patches, backups and scaling.

Firebolt solves impossible data problems with extreme speed and elasticity on any scale. Firebolt has completely redesigned its cloud data warehouse to provide an extremely fast and efficient analytics experience at all scales. You can analyze more data at higher levels of detail with lightning fast queries, which is an order-of-magnitude improvement in performance. You can easily scale up or decrease to support any workload, data amount, and concurrent users. Firebolt believes data warehouses should be easier than we are used to. We strive to make everything that was previously difficult and labor-intensive, simple. Cloud data warehouse providers make money from the cloud resources that you use. We don't! Finally, a pricing system that is fair, transparent, and allows for scale without breaking the bank.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

Amazon Athena allows you to easily analyze data in Amazon S3 with standard SQL. Athena is serverless so there is no infrastructure to maintain and you only pay for the queries you run. Athena is simple to use. Simply point to your data in Amazon S3 and define the schema. Then, you can query standard SQL. Most results are delivered in a matter of seconds. Athena makes it easy to prepare your data for analysis without the need for complicated ETL jobs. Anyone with SQL skills can quickly analyze large-scale data sets. Athena integrates with AWS Glue Data Catalog out-of-the box. This allows you to create a unified metadata repositorie across multiple services, crawl data sources and discover schemas. You can also populate your Catalog by adding new and modified partition and table definitions. Schema versioning is possible.