Alternatives to Materialize

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.

RisingWave is an open-source distributed SQL streaming database released under Apache 2.0 license. RisingWave is PostgreSQL-compatible, and allows users to process streaming data using standard SQL. Written in Rust and designed with cloud-native architecture, RisingWave can achieve 10X better performance and cost efficiency compared to conventional stream processing systems. RisingWave Cloud is a fully managed cloud service. Users can leverage RisingWave Cloud to process streaming data and serve analytical queries at ease.

Data integration for hybrid clouds Modern, reliable data integration across both your private cloud and public cloud. All this in real-time, with change data capture and streams. Striim was developed by the executive and technical team at GoldenGate Software. They have decades of experience in mission critical enterprise workloads. Striim can be deployed in your environment as a distributed platform or in the cloud. Your team can easily adjust the scaleability of Striim. Striim is fully secured with HIPAA compliance and GDPR compliance. Built from the ground up to support modern enterprise workloads, whether they are hosted in the cloud or on-premise. Drag and drop to create data flows among your sources and targets. Real-time SQL queries allow you to process, enrich, and analyze streaming data.

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.

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.

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.

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.

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.

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 Kafka®, with Confluent, has an infinite retention. Be infrastructure-enabled, not infrastructure-restricted Legacy technologies require you to choose between being real-time or highly-scalable. Event streaming allows you to innovate and win by being both highly-scalable and real-time. Ever wonder how your rideshare app analyses massive amounts of data from multiple sources in order to calculate real-time ETA. Wondering how your credit card company analyzes credit card transactions from all over the world and sends fraud notifications in real time? Event streaming is the answer. Microservices are the future. A persistent bridge to the cloud can enable your hybrid strategy. Break down silos to demonstrate compliance. Gain real-time, persistent event transport. There are many other options.

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.

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 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.

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.

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.

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.

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.

AI-powered Informatica Data Engineering streaming allows data engineers to ingest and process real-time streaming data in order to gain actionable insights.

You can deliver customer experiences like never before with breakthrough data streaming capabilities Both the ecosystem and Kafka API are compatible. Redpanda BulletPredictable low latency with zero data loss. Redpanda BulletUp to 10x faster than Kafka Redpanda BulletEnterprise-grade support and hotfixes. Redpanda BulletAutomated backups for S3/GCS. Redpanda Bullet100% freedom of routine Kafka operations. Redpanda BulletSupports for AWS/GCP. Redpanda was built from the ground up to be easy to install and get running quickly. Redpanda's power will be evident once you have tried it in production. You can use the more advanced Redpanda functions. We manage all aspects of provisioning, monitoring, as well as upgrades. We do not have access to your cloud credentials. Sensitive data never leaves your environment. You can have it provisioned, operated, maintained, and updated for you. Configurable instance types. As your needs change, you can expand the cluster.

You can manage your data from the edge to the cloud with a simple, no-code approach to creating sophisticated streaming applications.

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.

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.

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.

IBM Streams analyzes a wide range of streaming data, including unstructured text, video and audio, and geospatial and sensor data. This helps organizations to spot opportunities and risks, and make decisions in real-time.

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.

Azure Stream Analytics is an easy-to-use, real time analytics service that's designed for mission-critical workloads. In just a few steps, you can create an end-to-end streaming pipeline that is serverless in just a few clicks. SQL--easily extensible and customizable with custom code, built-in machine learning capabilities and more advanced scenarios. You can run the most complex workloads with confidence knowing that your SLA is financially backed.

HarperDB is an integrated distributed systems platform which combines database, caching and application functions into one technology. It allows you to deliver global back-end services at a lower cost, with higher performance and less effort. Install user-programmed apps and pre-built additions on top of data for a back end with ultra-low latencies. Distributed database with a high throughput per second, delivering orders of magnitude higher than NoSQL alternatives. Native real-time pub/sub data processing and communication via MQTT interfaces, WebSockets, and HTTP interfaces. HarperDB provides powerful data-in motion capabilities without adding additional services such as Kafka. Focus on features that will help your business grow, rather than fighting complicated infrastructure. You can't slow down the speed of light but you can reduce the amount of light between your users' data and them.

You can quickly collect, process, analyze, and analyze video and data streams. Amazon Kinesis makes it easy for you to quickly and easily collect, process, analyze, and interpret streaming data. Amazon Kinesis provides key capabilities to process streaming data at any scale cost-effectively, as well as the flexibility to select the tools that best fit your application's requirements. Amazon Kinesis allows you to ingest real-time data, including video, audio, website clickstreams, application logs, and IoT data for machine learning, analytics, or other purposes. Amazon Kinesis allows you to instantly process and analyze data, rather than waiting for all the data to be collected before processing can begin. Amazon Kinesis allows you to ingest buffer and process streaming data instantly, so you can get insights in seconds or minutes, instead of waiting for hours or days.

Unified stream and batch data processing that is serverless, fast, cost-effective, and low-cost. Fully managed data processing service. Automated provisioning of and management of processing resource. Horizontal autoscaling worker resources to maximize resource use Apache Beam SDK is an open-source platform for community-driven innovation. Reliable, consistent processing that works exactly once. Streaming data analytics at lightning speed Dataflow allows for faster, simpler streaming data pipeline development and lower data latency. Dataflow's serverless approach eliminates the operational overhead associated with data engineering workloads. Dataflow allows teams to concentrate on programming and not managing server clusters. Dataflow's serverless approach eliminates operational overhead from data engineering workloads, allowing teams to concentrate on programming and not managing server clusters. Dataflow automates provisioning, management, and utilization of processing resources to minimize latency.

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.

In the field of IoT stream processing and analytics, SQLstream ranks #1 according to ABI Research. Used by Verizon, Walmart, Cisco, and Amazon, our technology powers applications on premises, in the cloud, and at the edge. SQLstream enables time-critical alerts, live dashboards, and real-time action with sub-millisecond latency. Smart cities can reroute ambulances and fire trucks or optimize traffic light timing based on real-time conditions. Security systems can detect hackers and fraudsters, shutting them down right away. AI / ML models, trained with streaming sensor data, can predict equipment failures. Thanks to SQLstream's lightning performance -- up to 13 million rows / second / CPU core -- companies have drastically reduced their footprint and cost. Our efficient, in-memory processing allows operations at the edge that would otherwise be impossible. Acquire, prepare, analyze, and act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code, GUI dev environment. Edit scripts instantly and view instantaneous results without compiling. Deploy with native Kubernetes support. Easy installation includes Docker, AWS, Azure, Linux, VMWare, and more

ScaleOut Digital Twin Streaming Service™ Easily create and deploy real-time twins for streaming analytics Connect with many data sources with Azure & AWS IoT Hubs, Kafka, etc. Maximize situational awareness through live, aggregate analytics. A breakthrough cloud service that simultaneously tracks telemetry across millions of data sources, with "real-time digital twins" -- enabling deep, immediate introspection and state-tracking for thousands of devices. The powerful UI makes deployment easy and displays aggregate analytics in real-time to maximize situational awareness. Ideal for a wide variety of applications, including the Internet of Things, real-time intelligent monitoring and logistics, financial services, and financial services. Simple pricing makes it easy to get started. The ScaleOut Digital Twin Builder software and ScaleOut Digital Twin Streaming Service enable the next generation of stream processing.

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.

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.

With your own data shop, create new revenue streams from the data you already have. Narrative focuses on the fundamental principles that make buying or selling data simpler, safer, and more strategic. You must ensure that the data you have access to meets your standards. It is important to know who and how the data was collected. Access new supply and demand easily for a more agile, accessible data strategy. You can control your entire data strategy with full end-to-end access to all inputs and outputs. Our platform automates the most labor-intensive and time-consuming aspects of data acquisition so that you can access new data sources in days instead of months. You'll only ever have to pay for what you need with filters, budget controls and automatic deduplication.

Apama Streaming Analytics enables organizations to analyze and respond to IoT and fast moving data in real time, allowing them to react intelligently to events as they occur. Apama Community Edition by Software AG is a freemium version that allows users to learn about, develop, and implement streaming analytics applications. Software AG Data & Analytics Platform offers an integrated, modular and end-to-end set of world-class capabilities that are optimized for high-speed data management. It also provides connectivity and integration to all major enterprise data sources. You can choose the capabilities that you require: streaming, predictive, and visual analytics. There is also messaging and integration with other enterprise applications. You can integrate historical and other data to create models and enrich customer data.

IBM Db2®, a family of hybrid data management tools, offers a complete suite AI-empowered capabilities to help you manage structured and unstructured data both on premises and in private and public clouds. Db2 is built upon an intelligent common SQL engine that allows for flexibility and scalability.

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.

Apache Flink is a distributed processing engine and framework for stateful computations using unbounded and bounded data streams. Flink can be used in all cluster environments and perform computations at any scale and in-memory speed. A stream of events can be used to produce any type of data. All data, including credit card transactions, machine logs, sensor measurements, and user interactions on a website, mobile app, are generated as streams. Apache Flink excels in processing both unbounded and bound data sets. Flink's runtime can run any type of application on unbounded stream streams thanks to its precise control of state and time. Bounded streams are internal processed by algorithms and data structure that are specifically designed to process fixed-sized data sets. This results in excellent performance. Flink can be used with all of the resource managers previously mentioned.

Amazon MSK is a fully managed service that makes coding and running applications that use Apache Kafka for streaming data processing easy. Apache Kafka is an open source platform that allows you to build real-time streaming data applications and pipelines. Amazon MSK allows you to use native Apache Kafka APIs for populating data lakes, stream changes between databases, and to power machine learning or analytics applications. It is difficult to set up, scale, and manage Apache Kafka clusters in production. Apache Kafka clusters can be difficult to set up and scale on your own.

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.

Apache Kafka®, is an open-source distributed streaming platform.

Xeotek is a powerful desktop and web application that helps companies explore and develop data streams and applications faster. Xeotek KaDeck was created for developers, business users, and operations personnel. KaDeck gives business users, developers, operations, and business users insight into data and processes. This benefits the entire team: less misunderstandings, less work, more transparency. Xeotek KaDeck gives you control over your data streams. You can save hours by getting insights at the application and data level in projects or your day-to-day operations. KaDeck makes it easy to export, filter, transform, and manage data streams. You can run JavaScript (NodeV4) code and transform & create test data. You can also view & modify consumer offsets. Manage your streams or topics, Kafka Connect instance, schema registry, ACLs, and Kafka Connect topics from one user interface.

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.

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.

Allow everyone to view and discover streaming data. Up to 95% of productivity can be increased by sharing, documenting, and cataloging data. Next, create apps for production use cases using the data. To address privacy concerns and cover all the gaps in open source technology, apply a data-centric security approach. Secure and low-code data pipeline capabilities. All darkness is eliminated and data and apps can be viewed with unparalleled visibility. Unify your data technologies and data meshes and feel confident using open source production. Independent third-party reviews have rated Lenses the best product for real time stream analytics. We have built features to allow you to focus on what is driving value from real-time data. This was based on feedback from our community as well as thousands of engineering hours. You can deploy and run SQL-based real-time applications over any Kafka Connect, Kubernetes or Kubernetes infrastructure, including AWS EKS.

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.

Your data models can be created, edited, and organized. You don't need another data tool to manage your data models. Weld allows you to create and manage them. It is packed with features that make it easy to create your data models: smart autocomplete, code folding and error highlighting, audit logs and version control, collaboration, and version control. We use the same text editor that VS Code - it is fast, powerful, and easy to read. Your queries are organized in a searchable and easily accessible library. Audit logs allow you to see when and by whom the query was last updated. Weld Model allows you to materialize models as views, tables, incremental tables, and views. You can also create custom materializations of your design. With the help of a dedicated team, you can manage all your data operations from one platform.

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.