Alternatives to VeloDB

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.

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.

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

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.

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.

Materialize is a reactive database that provides incremental view updates. Our standard SQL allows developers to easily work with streaming data. Materialize connects to many external data sources without any pre-processing. Connect directly to streaming sources such as Kafka, Postgres databases and CDC or historical data sources such as files or S3. Materialize allows you to query, join, and transform data sources in standard SQL - and presents the results as incrementally-updated Materialized views. Queries are kept current and updated as new data streams are added. With incrementally-updated views, developers can easily build data visualizations or real-time applications. It is as easy as writing a few lines SQL to build with streaming data.

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.

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.

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.

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.

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.

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.

StarRocks offers at least 300% more performance than other popular solutions, whether you're using a single or multiple tables. With a rich set connectors, you can ingest real-time data into StarRocks for the latest insights. A query engine that adapts your use cases. StarRocks allows you to scale your analytics easily without moving your data or rewriting SQL. StarRocks allows a rapid journey between data and insight. StarRocks is unmatched in performance and offers a unified OLAP system that covers the most common data analytics scenarios. StarRocks offers at least 300% faster performance than other popular solutions, whether you are working with one table or many. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

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.

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.

Aerospike is the global leader for next-generation, real time NoSQL data solutions at any scale. Aerospike helps enterprises overcome seemingly impossible data bottlenecks and compete with other companies at a fraction of the cost and complexity of legacy NoSQL databases. Aerospike's Hybrid Memory Architecture™ is a patented technology that unlocks the full potential of modern hardware and delivers previously unimaginable value. It does this by delivering unimaginable value from huge amounts of data at both the edge, core, and in the cloud. Aerospike empowers customers with the ability to instantly combat fraud, dramatically increase shopping cart sizes, deploy global digital payment networks, and provide instant, one-to-1 personalization for millions. Aerospike customers include Airtel and Banca d'Italia as well as Snap, Verizon Media, Wayfair, PayPal, Snap, Verizon Media, and Nielsen. The company's headquarters is in Mountain View, California. Additional locations are in London, Bengaluru, India, and Tel Aviv in Israel.

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.

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.

Spark Streaming uses Apache Spark's language-integrated API for stream processing. It allows you to write streaming jobs in the same way as you write batch jobs. It supports Java, Scala, and Python. Spark Streaming recovers lost work as well as operator state (e.g. Without any additional code, Spark Streaming recovers both lost work and operator state (e.g. sliding windows) right out of the box. Spark Streaming allows you to reuse the same code for batch processing and join streams against historical data. You can also run ad-hoc queries about stream state by running on Spark. Spark Streaming allows you to create interactive applications that go beyond analytics. Apache Spark includes Spark Streaming. It is updated with every Spark release. Spark Streaming can be run on Spark's standalone mode or other supported cluster resource mangers. It also has a local run mode that can be used for development. Spark Streaming uses ZooKeeper for high availability in production.

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.

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.

PySpark is a Python interface for Apache Spark. It allows you to create Spark applications using Python APIs. Additionally, it provides the PySpark shell that allows you to interactively analyze your data in a distributed environment. PySpark supports Spark's most popular features, including Spark SQL, DataFrame and Streaming. Spark SQL is a Spark module that allows structured data processing. It can be used as a distributed SQL query engine and a programming abstraction called DataFrame. The streaming feature in Apache Spark, which runs on top of Spark allows for powerful interactive and analytic applications across streaming and historical data. It also inherits Spark's ease-of-use and fault tolerance characteristics.

Apache Hive™, a data warehouse software, facilitates the reading, writing and management of large datasets that are stored in distributed storage using SQL. Structure can be projected onto existing data. Hive provides a command line tool and a JDBC driver to allow users to connect to it. Apache Hive is an Apache Software Foundation open-source project. It was previously a subproject to Apache® Hadoop®, but it has now become a top-level project. We encourage you to read about the project and share your knowledge. To execute traditional SQL queries, you must use the MapReduce Java API. Hive provides the SQL abstraction needed to integrate SQL-like query (HiveQL), into the underlying Java. This is in addition to the Java API that implements queries.

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.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

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.

Flume is a reliable, distributed service that efficiently collects, aggregates, and moves large amounts of log data. Flume's architecture is based on streaming data flows and is simple and flexible. It is robust and fault-tolerant, with many failovers and recovery options. It is based on a simple extensible data structure that allows for online analytical applications. Flume 1.8.0 has been released by the Apache Flume team. Flume is a distributed, reliable and available service that efficiently collects, aggregates, and moves large amounts of streaming event information.

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.

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.

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.

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.

Samza lets you build stateful applications that can process data in real time from multiple sources, including Apache Kafka. It has been battle-tested at scale and supports flexible deployment options, including running on YARN or as a standalone program. Samza offers high throughput and low latency to instantly analyze your data. With features like host-affinity and incremental checkpoints, Samza can scale to many terabytes in state. Samza is easy-to-use with flexible deployment options YARN, Kubernetes, or standalone. The ability to run the same code to process streaming and batch data. Integrates with multiple sources, including Kafka and HDFS, AWS Kinesis Azure Eventhubs, Azure Eventhubs K-V stores, ElasticSearch, AWS Kinesis, Kafka and ElasticSearch.

Amazon Managed Service For Apache Flink is used by thousands of customers to run stream-processing applications. Amazon Managed Service Apache Flink allows you to transform and analyze streaming data using Apache Flink in real-time and integrate applications with AWS services. There are no clusters or servers to manage and no computing infrastructure to install. You only pay for the resources that you use. You can build and run Apache Flink apps without having to manage resources or clusters, or set up infrastructure. Process gigabytes per second, with latencies of subseconds and respond to events instantly. Multi-AZ deployments, APIs for lifecycle management and APIs to manage application lifecycles help you deploy highly available and durable apps. Create applications that transform data and deliver it to Amazon Simple Storage Service (Amazon S3) and Amazon OpenSearch Service.

Easy to capture, transform and load streaming data. Create a stream of data, select the destination and start streaming real time data in just a few simple clicks. Automate the provisioning and scaling of compute, memory and network resources, without any ongoing administration. Transform streaming data into formats such as Apache Parquet and dynamically partition streaming without building your own pipelines. Amazon Data Firehose is the fastest way to acquire data streams, transform them, and then deliver them to data lakes, warehouses, or analytics services. Amazon Data Firehose requires you to create a stream that includes a destination, a source and the transformations required. Amazon Data Firehose continuously processes a stream, scales automatically based on data availability, and delivers the results within seconds. Select the source of your data stream, or write data with the Firehose Direct PUT (API) API.

DeltaStream is an integrated serverless streaming processing platform that integrates seamlessly with streaming storage services. Imagine it as a compute layer on top your streaming storage. It offers streaming databases and streaming analytics along with other features to provide an integrated platform for managing, processing, securing and sharing streaming data. DeltaStream has a SQL-based interface that allows you to easily create stream processing apps such as streaming pipelines. It uses Apache Flink, a pluggable stream processing engine. DeltaStream is much more than a query-processing layer on top Kafka or Kinesis. It brings relational databases concepts to the world of data streaming, including namespacing, role-based access control, and enables you to securely access and process your streaming data, regardless of where it is stored.

Change data capture and replication service that is serverless and easy to use. Access to streaming data in MySQL, PostgreSQL and AlloyDB databases. BigQuery offers near-real-time analytics. Easy-to use setup with built-in security connectivity for faster time to value. A serverless platform which automatically scales without the need to provision or manage resources. Log-based mechanism reduces the load on source databases and any potential disruption. Synchronize data reliably across heterogeneous storage systems, databases, and applications with low latency while minimising impact on source performance. Easy-to-use and serverless service that scales up and down seamlessly and does not require infrastructure management will get you up and running quickly. Connect and integrate your data with the best Google Cloud services, including BigQuery, Spanner Dataflow and Data Fusion.

Streaming service is a streaming service that allows developers and data scientists to stream real-time events. It is serverless and Apache Kafka compatible. Streaming can be integrated with Oracle Cloud Infrastructure, Database, GoldenGate, Integration Cloud, and Oracle Cloud Infrastructure (OCI). The service provides integrations for hundreds third-party products, including databases, big data, DevOps, and SaaS applications. Data engineers can easily create and manage big data pipelines. Oracle manages all infrastructure and platform management, including provisioning, scaling and security patching. Streaming can provide state management to thousands of consumers with the help of consumer groups. This allows developers to easily create applications on a large scale.

No more low-level code or gluing together complex systems. SQL makes it easy to build and deploy pipelines quickly. Data engineering service that allows developers and data engineers to quickly build and deploy data pipelines for data-driven apps. It is easy to connect to and find available data using pre-built connectors for messaging, storage, and database engines. Each connection you make will result in a stream of data to or from the system. You can create your pipelines using SQL with Decodable. Pipelines use streams to send and receive data to and from your connections. Streams can be used to connect pipelines to perform the most difficult processing tasks. To ensure data flows smoothly, monitor your pipelines. Create curated streams that can be used by other teams. To prevent data loss due to system failures, you should establish retention policies for streams. You can monitor real-time performance and health metrics to see if everything is working.

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.

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.

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.

Apache Storm is an open-source distributed realtime computing system that is free and open-source. Apache Storm makes it simple to process unbounded streams and data reliably, much like Hadoop did for batch processing. Apache Storm is easy to use with any programming language and is a lot fun! Apache Storm can be used for many purposes: realtime analytics and online machine learning. It can also be used with any programming language. Apache Storm is fast. A benchmark measured it at more than a million tuples per second per node. It is highly scalable, fault-tolerant and guarantees that your data will be processed. It is also easy to set up. Apache Storm can be integrated with the queueing and databases technologies you already use. Apache Storm topology processes streams of data in arbitrarily complex ways. It also partitions the streams between each stage of the computation as needed. Learn more in the tutorial.

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

Perhaps you want to allow your clients to access their LIMS data via the internet. AIS labPortal makes it possible to do exactly that. Sample analyses can be emailed to customers, but not as paper copies. Clients can access their data using their unique login and security code from their computer. This is safer, faster, and more environmentally friendly than sending paper copies of sample analyses to customers. labPortal, a web-based portal, securely stores client's sample information and data in a cloud. Clients can access this data instantly from any computer, tablet, or smartphone. LabPortal's interface is an 'inbox' design that is simple and easy to use. It features an enhanced query engine, conditional highlight and Microsoft Excel export. The software includes an easy-to-use sample registration tool that allows users to preregister samples online. Transcribing data can be tedious and time-consuming.

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.

Responsive apps keep developers motivated and users engaged. With the DataStax Astra streaming service platform, you can meet these ever-increasing demands. DataStax Astra Streaming, powered by Apache Pulsar, is a cloud-native messaging platform and event streaming platform. Astra Streaming lets you build streaming applications on top a multi-cloud, elastically scalable and event streaming platform. Apache Pulsar is the next-generation event streaming platform that powers Astra Streaming. It provides a unified solution to streaming, queuing and stream processing. Astra Streaming complements Astra DB. Astra Streaming allows existing Astra DB users to easily create real-time data pipelines from and to their Astra DB instances. Astra Streaming allows you to avoid vendor lock-in by deploying on any major public cloud (AWS, GCP or Azure) compatible with open source Apache Pulsar.

GeoSpock DB - The space-time analytics database - allows data fusion in the connected world. GeoSpockDB is a unique cloud-native database that can be used to query for real-world applications. It can combine multiple sources of Internet of Things data to unlock their full potential, while simultaneously reducing complexity, cost, and complexity. GeoSpock DB enables data fusion and efficient storage. It also allows you to run ANSI SQL query and connect to analytics tools using JDBC/ODBC connectors. Users can perform analysis and share insights with familiar toolsets. This includes support for common BI tools such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as Data Science and Machine Learning environments (including Python Notebooks or Apache Spark). The database can be integrated with internal applications as well as web services, including compatibility with open-source visualisation libraries like Cesium.js and Kepler.

Snowflake's platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything "just works." Learn more about Snowflake's AI Data Cloud at snowflake.com (NYSE: SNOW)

WarpStream, an Apache Kafka compatible data streaming platform, is built directly on object storage. It has no inter-AZ network costs, no disks that need to be managed, and it's infinitely scalable within your VPC. WarpStream is deployed in your VPC as a stateless, auto-scaling binary agent. No local disks are required to be managed. Agents stream data directly into and out of object storage without buffering on local drives and no data tiering. Instantly create new "virtual" clusters in our control plan. Support multiple environments, teams or projects without having to manage any dedicated infrastructure. WarpStream is Apache Kafka protocol compatible, so you can continue to use your favorite tools and applications. No need to rewrite or use a proprietary SDK. Simply change the URL of your favorite Kafka library in order to start streaming. Never again will you have to choose between budget and reliability.

Pipes is a new way of creating queries and shaping data. It's inspired by Python Notebooks. This is a simplified way to increase performance without sacrificing complexity. Splitting your query into multiple nodes makes it easier to develop and maintain. You can activate your production-ready API endpoints in one click. Transforms happen on-the-fly, so you always have the most current data. You can share secure access to your data with one click, and get consistent results. Tinybird scales linearly, so don't worry if you have high traffic. Imagine if you could transform any Data Stream or CSV file into a secure real-time analytics API endpoint in a matter minutes. We believe in high-frequency decision making for all industries, including retail, manufacturing and telecommunications.