Alternatives to Tinybird

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.

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.

It takes only days to wrap any data source with a single reference Data API and simplify access to reporting and analytics data across your teams. Make it easy for application developers and data engineers to access the data from any source in a streamlined manner. - The single schema-less Data API endpoint - Review, configure metrics and dimensions in one place via UI - Data model visualization to make faster decisions - Data Export management scheduling API Our proxy perfectly fits into your current API management ecosystem (versioning, data access, discovery) no matter if you are using Mulesoft, Apigee, Tyk, or your homegrown solution. Leverage the capabilities of Data API and enrich your products with self-service analytics for dashboards, data Exports, or custom report composer for ad-hoc metric querying. Ready-to-use Report Builder and JavaScript components for popular charting libraries (Highcharts, BizCharts, Chart.js, etc.) makes it easy to embed data-rich functionality into your products. Your product or service users will love that because everybody likes to make data-driven decisions! And you will not have to make custom report queries anymore!

Spark Streaming extends the capabilities of Apache Spark by integrating its language-based API for stream processing, allowing you to create streaming applications in the same manner as batch applications. This powerful tool is compatible with Java, Scala, and Python. One of its key features is the automatic recovery of lost work and operator state, such as sliding windows, without requiring additional code from the user. By leveraging the Spark framework, Spark Streaming enables the reuse of the same code for batch processes, facilitates the joining of streams with historical data, and supports ad-hoc queries on the stream's state. This makes it possible to develop robust interactive applications rather than merely focusing on analytics. Spark Streaming is an integral component of Apache Spark, benefiting from regular testing and updates with each new release of Spark. Users can deploy Spark Streaming in various environments, including Spark's standalone cluster mode and other compatible cluster resource managers, and it even offers a local mode for development purposes. For production environments, Spark Streaming ensures high availability by utilizing ZooKeeper and HDFS, providing a reliable framework for real-time data processing. This combination of features makes Spark Streaming an essential tool for developers looking to harness the power of real-time analytics efficiently.

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

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

Xano offers a fully-managed, scaleable infrastructure that powers your backend. You can also quickly create the business logic that powers your backend with Xano without writing a single line or use one our pre-made templates to launch quickly and without compromising security or scale. You can quickly create custom API endpoints with just one line of code. Our out-of-the box CRUD operations, Marketplace extensions and templates will accelerate your time to market. Your API is "ready-to use" so you can connect to any frontend immediately and concentrate on your business logic. Swagger automatically documents everything so that you can connect to any frontend easily. Xano uses PostgreSQL, which offers the flexibility of a relational and the Big data needs that a NoSQL solution. You can add features to your backend with just a few clicks. Or, you can use pre-made templates and extensions to jumpstart the project.

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

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

A user-friendly, serverless service for change data capture and replication that provides access to streaming data from a variety of databases including MySQL, PostgreSQL, AlloyDB, SQL Server, and Oracle. This solution enables near real-time analytics in BigQuery, allowing for quick insights and decision-making. With a straightforward setup that includes built-in secure connectivity, organizations can achieve faster time-to-value. The platform is designed to scale automatically, eliminating the need for resource provisioning or management. Utilizing a log-based mechanism, it minimizes the load and potential disruptions on source databases, ensuring smooth operation. This service allows for reliable data synchronization across diverse databases, storage systems, and applications, while keeping latency low and reducing any negative impact on source performance. Organizations can quickly activate the service, enjoying the benefits of a scalable solution with no infrastructure overhead. Additionally, it facilitates seamless data integration across the organization, leveraging the power of Google Cloud services such as BigQuery, Spanner, Dataflow, and Data Fusion, thus enhancing overall operational efficiency and data accessibility. This comprehensive approach not only streamlines data processes but also empowers teams to make informed decisions based on timely data insights.

Materialize is an innovative reactive database designed to provide updates to views incrementally. It empowers developers to seamlessly work with streaming data through the use of standard SQL. One of the key advantages of Materialize is its ability to connect directly to a variety of external data sources without the need for pre-processing. Users can link to real-time streaming sources such as Kafka, Postgres databases, and change data capture (CDC), as well as access historical data from files or S3. The platform enables users to execute queries, perform joins, and transform various data sources using standard SQL, presenting the outcomes as incrementally-updated Materialized views. As new data is ingested, queries remain active and are continuously refreshed, allowing developers to create data visualizations or real-time applications with ease. Moreover, constructing applications that utilize streaming data becomes a straightforward task, often requiring just a few lines of SQL code, which significantly enhances productivity. With Materialize, developers can focus on building innovative solutions rather than getting bogged down in complex data management tasks.

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.

The Hitachi Streaming Data Platform (SDP) is engineered for real-time processing of extensive time-series data as it is produced. Utilizing in-memory and incremental computation techniques, SDP allows for rapid analysis that circumvents the typical delays experienced with conventional stored data processing methods. Users have the capability to outline summary analysis scenarios through Continuous Query Language (CQL), which resembles SQL, thus enabling adaptable and programmable data examination without requiring bespoke applications. The platform's architecture includes various components such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, which together create a scalable and efficient data processing ecosystem. Additionally, SDP’s modular framework accommodates multiple data input and output formats, including text files and HTTP packets, and seamlessly integrates with visualization tools like RTView for real-time performance monitoring. This comprehensive design ensures that users can effectively manage and analyze data streams as they occur.

Apache Storm is a distributed computation system that is both free and open source, designed for real-time data processing. It simplifies the reliable handling of endless data streams, similar to how Hadoop revolutionized batch processing. The platform is user-friendly, compatible with various programming languages, and offers an enjoyable experience for developers. With numerous applications including real-time analytics, online machine learning, continuous computation, distributed RPC, and ETL, Apache Storm proves its versatility. It's remarkably fast, with benchmarks showing it can process over a million tuples per second on a single node. Additionally, it is scalable and fault-tolerant, ensuring that data processing is both reliable and efficient. Setting up and managing Apache Storm is straightforward, and it seamlessly integrates with existing queueing and database technologies. Users can design Apache Storm topologies to consume and process data streams in complex manners, allowing for flexible repartitioning between different stages of computation. For further insights, be sure to explore the detailed tutorial available.

With your data now actively flowing, it's essential to extract meaningful insights from it. Stream processing allows for immediate analysis of your data streams, though establishing the necessary infrastructure can be a daunting task. To address this challenge, Confluent has introduced ksqlDB, a database specifically designed for applications that require stream processing. By continuously processing data streams generated across your organization, you can turn your data into actionable insights right away. ksqlDB features an easy-to-use syntax that facilitates quick access to and enhancement of data within Kafka, empowering development teams to create real-time customer experiences and meet operational demands driven by data. This platform provides a comprehensive solution for gathering data streams, enriching them, and executing queries on newly derived streams and tables. As a result, you will have fewer infrastructure components to deploy, manage, scale, and secure. By minimizing the complexity in your data architecture, you can concentrate more on fostering innovation and less on technical maintenance. Ultimately, ksqlDB transforms the way businesses leverage their data for growth and efficiency.

Effortlessly capture, modify, and transfer streaming data in real time. You can create a delivery stream, choose your desired destination, and begin streaming data with minimal effort. The system automatically provisions and scales necessary compute, memory, and network resources without the need for continuous management. You can convert raw streaming data into various formats such as Apache Parquet and dynamically partition it without the hassle of developing your processing pipelines. Amazon Data Firehose is the most straightforward method to obtain, transform, and dispatch data streams in mere seconds to data lakes, data warehouses, and analytics platforms. To utilize Amazon Data Firehose, simply establish a stream by specifying the source, destination, and any transformations needed. The service continuously processes your data stream, automatically adjusts its scale according to the data volume, and ensures delivery within seconds. You can either choose a source for your data stream or utilize the Firehose Direct PUT API to write data directly. This streamlined approach allows for greater efficiency and flexibility in handling data streams.

Engaging applications captivate users while motivating developers to innovate. To meet the growing demands of the digital landscape, consider utilizing the DataStax Astra Streaming service platform. This cloud-native platform for messaging and event streaming is built on the robust foundation of Apache Pulsar. With Astra Streaming, developers can create streaming applications that leverage a multi-cloud, elastically scalable architecture. Powered by the advanced capabilities of Apache Pulsar, this platform offers a comprehensive solution that encompasses streaming, queuing, pub/sub, and stream processing. Astra Streaming serves as an ideal partner for Astra DB, enabling current users to construct real-time data pipelines seamlessly connected to their Astra DB instances. Additionally, the platform's flexibility allows for deployment across major public cloud providers, including AWS, GCP, and Azure, thereby preventing vendor lock-in. Ultimately, Astra Streaming empowers developers to harness the full potential of their data in real-time environments.

Nussknacker allows domain experts to use a visual tool that is low-code to help them create and execute real-time decisioning algorithm instead of writing code. It is used to perform real-time actions on data: real-time marketing and fraud detection, Internet of Things customer 360, Machine Learning inferring, and Internet of Things customer 360. A visual design tool for decision algorithm is an essential part of Nussknacker. It allows non-technical users, such as analysts or business people, to define decision logic in a clear, concise, and easy-to-follow manner. With a click, scenarios can be deployed for execution once they have been created. They can be modified and redeployed whenever there is a need. Nussknacker supports streaming and request-response processing modes. It uses Kafka as its primary interface in streaming mode. It supports both stateful processing and stateless processing.

Aiven takes the reins on your open-source data infrastructure hosted in the cloud, allowing you to focus on what you excel at: developing applications. While you channel your energy into innovation, we expertly handle the complexities of managing cloud data infrastructure. Our solutions are entirely open source, providing the flexibility to transfer data between various clouds or establish multi-cloud setups. You will have complete visibility into your expenses, with a clear understanding of costs as we consolidate networking, storage, and basic support fees. Our dedication to ensuring your Aiven software remains operational is unwavering; should any challenges arise, you can count on us to resolve them promptly. You can launch a service on the Aiven platform in just 10 minutes and sign up without needing to provide credit card information. Simply select your desired open-source service along with the cloud and region for deployment, pick a suitable plan—which includes $300 in free credits—and hit "Create service" to begin configuring your data sources. Enjoy the benefits of maintaining control over your data while leveraging robust open-source services tailored to your needs. With Aiven, you can streamline your cloud operations and focus on driving your projects forward.

Achieve response times of around 40ms globally, ensuring your users enjoy the speed they deserve. Safeguard your API against unexpected traffic surges and downtime, building a reputation that users can depend on consistently. Tackle stability challenges with features like auto retries and stale-while-revalidate, because consistency is key to success. Significantly lighten your origin load by as much as 95%, allowing you to effectively manage any traffic increase while minimizing downtime and cutting costs. Gain a real-time understanding of your API’s performance metrics, as data is crucial for enhancement. Adapt your schema based on insightful usage statistics, allowing you to make informed decisions with confidence. Identify the specific country, page, and user responsible for each request to obtain detailed insights and stay informed about all activities. Monitor origin response times for every query and mutation, pinpointing areas ripe for optimization. Stay ahead of performance declines and errors by being alerted as soon as your users notice them, enabling swift resolutions. Keep a close watch on all HTTP and GraphQL errors to understand when and where users encounter problems, allowing you to address issues effectively and enhance overall user satisfaction. Regularly analyzing this data will foster continuous improvement in your API's reliability and performance.

CData Python Connectors make it easy for Python users to connect to SaaS and Big Data, NoSQL and relational data sources. Our Python Connectors provide simple Python database interfaces to (DB-API), making them easy to connect to popular tools like Jupyter Notebook and SQLAlchemy. CData Python Connectors wrap SQL around APIs and data protocol, making it easier to access data from Python. It also allows Python users to connect more than 150 SaaS and Big Data data sources with advanced Python processing. The CData Python Connectors bridge a critical gap in Python tooling, providing consistent connectivity with data-centric interfaces for hundreds of SaaS/Cloud, NoSQL and Big Data sources. Download a 30-day free trial or learn more at: https://ancillary-proxy.atarimworker.io?url=https%3A%2F%2Fwww.cdata.com%2Fpython%2F

Directus is an Open Data Platform that allows you to manage any SQL database's content. It offers a powerful API layer to developers and an intuitive app for non-technical users. Directus is written entirely in JavaScript (primarily Node.js or Vue.js), and it is modular and extensible. This allows it to be tailored to your specific project needs. Our platform can be used to manage digital experiences as a headless CMS, a database client for democratizing information, or as a standalone web app for back-office CRM, inventory, business intelligence and project management.

Enhance your application development process with scalable GraphQL APIs. Many organizations opt for GraphQL to expedite their application creation, as it empowers front-end developers to efficiently query various databases, microservices, and APIs through a single GraphQL endpoint. AWS AppSync serves as a fully managed solution that simplifies the development of GraphQL APIs by taking care of the complex task of securely connecting to data sources such as AWS DynamoDB and Lambda. It also allows for easy integration of caching mechanisms to boost performance, real-time subscriptions for instant updates, and client-side data stores to ensure offline clients remain synchronized. Once your API is live, AWS AppSync will automatically adjust the capacity of your GraphQL API execution engine based on incoming request volumes, ensuring optimal performance. Moreover, AWS AppSync provides comprehensive management of both GraphQL APIs and Pub/Sub API setups, along with features like auto-scaling and high availability. The platform also offers built-in capabilities for securing, monitoring, logging, and tracing your API with services like AWS WAF, CloudWatch, and X-Ray, making it a robust choice for developers. This integrated approach not only streamlines development but also enhances overall application reliability and responsiveness.

Insigna - The complete Platform for Real-time Analytics and Data Management. Insigna offers integration, automated processing, transformation, data preparation and real-time analytics to derive and deliver intelligence to various stakeholders. Insigna enables connectivity with the most popular network communication protocols, data stores, enterprise applications, and cloud platforms. Coupled with a rich set of out-of-the-box data transformation capabilities, enterprises greatly benefit from the opportunities offered by operations data generated in real-time.

Codehooks is an innovative and user-friendly backend-as-a-service designed for building comprehensive API backends using JavaScript and Node.js. Experience rapid and effortless backend development without the need for configuration, utilizing serverless JavaScript, TypeScript, or Node.js, along with a built-in NoSQL document database, a key-value store, CRON jobs, and queue workers. The document database leverages RocksDB technology and offers a query language similar to that of MongoDB, enabling developers to efficiently manage and retrieve data. This platform is ideal for those looking to simplify their backend processes while maintaining high performance and flexibility.

Explore Azure Stream Analytics, a user-friendly real-time analytics solution tailored for essential workloads. Create a comprehensive serverless streaming pipeline effortlessly within a matter of clicks. Transition from initial setup to full production in mere minutes with SQL, which can be easily enhanced with custom code and integrated machine learning features for complex use cases. Rely on the assurance of a financially backed SLA as you handle your most challenging workloads, knowing that performance and reliability are prioritized. This service empowers organizations to harness real-time data effectively, ensuring timely insights and informed decision-making.

Streamkap is a modern streaming ETL platform built on top of Apache Kafka and Flink, designed to replace batch ETL with streaming in minutes. It enables data movement with sub-second latency using change data capture for minimal impact on source databases and real-time updates. The platform offers dozens of pre-built, no-code source connectors, automated schema drift handling, updates, data normalization, and high-performance CDC for efficient and low-impact data movement. Streaming transformations power faster, cheaper, and richer data pipelines, supporting Python and SQL transformations for common use cases like hashing, masking, aggregations, joins, and unnesting JSON. Streamkap allows users to connect data sources and move data to target destinations with an automated, reliable, and scalable data movement platform. It supports a broad range of event and database sources.

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

Informatica's AI-driven Data Engineering Streaming empowers data engineers to efficiently ingest, process, and analyze real-time streaming data, offering valuable insights. The advanced serverless deployment feature, coupled with an integrated metering dashboard, significantly reduces administrative burdens. With CLAIRE®-enhanced automation, users can swiftly construct intelligent data pipelines that include features like automatic change data capture (CDC). This platform allows for the ingestion of thousands of databases, millions of files, and various streaming events. It effectively manages databases, files, and streaming data for both real-time data replication and streaming analytics, ensuring a seamless flow of information. Additionally, it aids in the discovery and inventorying of all data assets within an organization, enabling users to intelligently prepare reliable data for sophisticated analytics and AI/ML initiatives. By streamlining these processes, organizations can harness the full potential of their data assets more effectively than ever before.

Transform your data into a real-time stream, ensuring it is instantly accessible and ready for utilization. Leo simplifies the complexities of event sourcing, allowing you to effortlessly create, visualize, monitor, and sustain your data streams. By unlocking your data, you free yourself from the limitations imposed by outdated systems. The significant reduction in development time leads to higher satisfaction among both developers and stakeholders alike. Embrace microservice architectures to foster continuous innovation and enhance your agility. Ultimately, achieving success with microservices hinges on effective data management. Organizations need to build a dependable and repeatable data backbone to turn microservices into a tangible reality. You can also integrate comprehensive search functionality into your custom application, as the continuous flow of data makes managing and updating a search database a seamless task. With these advancements, your organization will be well-positioned to leverage data more effectively than ever before.

Estuary Flow, a new DataOps platform, empowers engineering teams with the ability to build data-intensive real-time applications at scale and with minimal friction. This platform allows teams to unify their databases, pub/sub and SaaS systems around their data without having to invest in new infrastructure or development.

Flume is a dependable and distributed service designed to efficiently gather, aggregate, and transport significant volumes of log data. Its architecture is straightforward and adaptable, centered on streaming data flows, which enhances its usability. The system is built to withstand faults and includes various mechanisms for recovery and adjustable reliability features. Additionally, it employs a simple yet extensible data model that supports online analytic applications effectively. The Apache Flume team is excited to announce the launch of Flume version 1.8.0, which continues to enhance its capabilities. This version further solidifies Flume's role as a reliable tool for managing large-scale streaming event data efficiently.

Facilitates seamless data exchange among components within microservice architectures. When utilized as a communication method for microservices, it not only streamlines integration but also enhances reliability and scalability. The system allows for reading and writing data in nearly real-time, while providing the flexibility to set data throughput and storage durations according to specific requirements. Users can finely configure resources for processing data streams, accommodating anything from small streams of 100 KB/s to more substantial ones at 100 MB/s. Additionally, Yandex Data Transfer enables the delivery of a single stream to various targets with distinct retention policies. Data is automatically replicated across multiple availability zones that are geographically distributed, ensuring redundancy and accessibility. After the initial setup, managing data streams can be done centrally through either the management console or the API, offering convenient oversight. It also supports continuous data collection from diverse sources, including website browsing histories and application logs, making it a versatile tool for real-time analytics. Overall, Yandex Data Streams stands out for its robust capabilities in handling various data ingestion needs across different platforms.

IBM Streams analyzes a diverse array of streaming data, including unstructured text, video, audio, geospatial data, and sensor inputs, enabling organizations to identify opportunities and mitigate risks while making swift decisions. By leveraging IBM® Streams, users can transform rapidly changing data into meaningful insights. This platform evaluates various forms of streaming data, empowering organizations to recognize trends and threats as they arise. When integrated with other capabilities of IBM Cloud Pak® for Data, which is founded on a flexible and open architecture, it enhances the collaborative efforts of data scientists in developing models to apply to stream flows. Furthermore, it facilitates the real-time analysis of vast datasets, ensuring that deriving actionable value from your data has never been more straightforward. With these tools, organizations can harness the full potential of their data streams for improved outcomes.

Data processing that integrates both streaming and batch operations while being serverless, efficient, and budget-friendly. It offers a fully managed service for data processing, ensuring seamless automation in the provisioning and administration of resources. With horizontal autoscaling capabilities, worker resources can be adjusted dynamically to enhance overall resource efficiency. The innovation is driven by the open-source community, particularly through the Apache Beam SDK. This platform guarantees reliable and consistent processing with exactly-once semantics. Dataflow accelerates the development of streaming data pipelines, significantly reducing data latency in the process. By adopting a serverless model, teams can devote their efforts to programming rather than the complexities of managing server clusters, effectively eliminating the operational burdens typically associated with data engineering tasks. Additionally, Dataflow’s automated resource management not only minimizes latency but also optimizes utilization, ensuring that teams can operate with maximum efficiency. Furthermore, this approach promotes a collaborative environment where developers can focus on building robust applications without the distraction of underlying infrastructure concerns.

Cloudera DataFlow for the Public Cloud (CDF-PC) is a versatile, cloud-based data distribution solution that utilizes Apache NiFi, enabling developers to seamlessly connect to diverse data sources with varying structures, process that data, and deliver it to a wide array of destinations. This platform features a flow-oriented low-code development approach that closely matches the preferences of developers when creating, developing, and testing their data distribution pipelines. CDF-PC boasts an extensive library of over 400 connectors and processors that cater to a broad spectrum of hybrid cloud services, including data lakes, lakehouses, cloud warehouses, and on-premises sources, ensuring efficient and flexible data distribution. Furthermore, the data flows created can be version-controlled within a catalog, allowing operators to easily manage deployments across different runtimes, thereby enhancing operational efficiency and simplifying the deployment process. Ultimately, CDF-PC empowers organizations to harness their data effectively, promoting innovation and agility in data management.

Timeplus is an efficient, user-friendly stream processing platform that is both powerful and affordable. It comes packaged as a single binary, making it easy to deploy in various environments. Designed for data teams across diverse sectors, it enables the quick and intuitive processing of both streaming and historical data. With a lightweight design that requires no external dependencies, Timeplus offers comprehensive analytic capabilities for streaming and historical data. Its cost is just a fraction—1/10—of what similar open-source frameworks charge. Users can transform real-time market and transaction data into actionable insights seamlessly. The platform supports both append-only and key-value streams, making it ideal for monitoring financial information. Additionally, Timeplus allows the creation of real-time feature pipelines effortlessly. It serves as a unified solution for managing all infrastructure logs, metrics, and traces, which are essential for maintaining observability. Timeplus also accommodates a broad array of data sources through its user-friendly web console UI, while providing options to push data via REST API or to create external streams without the need to copy data into the platform. Overall, Timeplus offers a versatile and comprehensive approach to data processing for organizations looking to enhance their operational efficiency.

Say goodbye to the complexities of low-level coding and integrating intricate systems. With SQL, you can effortlessly construct and deploy data pipelines in mere minutes. This data engineering service empowers both developers and data engineers to easily create and implement real-time data pipelines tailored for data-centric applications. The platform provides ready-made connectors for various messaging systems, storage solutions, and database engines, simplifying the process of connecting to and discovering available data. Each established connection generates a stream that facilitates data movement to or from the respective system. Utilizing Decodable, you can design your pipelines using SQL, where streams play a crucial role in transmitting data to and from your connections. Additionally, streams can be utilized to link pipelines, enabling the management of even the most intricate processing tasks. You can monitor your pipelines to ensure a steady flow of data and create curated streams for collaborative use by other teams. Implement retention policies on streams to prevent data loss during external system disruptions, and benefit from real-time health and performance metrics that keep you informed about the operation's status, ensuring everything is running smoothly. Ultimately, Decodable streamlines the entire data pipeline process, allowing for greater efficiency and quicker results in data handling and analysis.

A live data platform based on Change Data Capture (CDC) facilitates the replication of diverse databases, supports real-time data integration, and assists in constructing a real-time data warehouse. By implementing CDC to synchronize production line information stored in DB2 and Oracle with a modern database, TapData has empowered an AI-enhanced real-time dispatch application that streamlines the semiconductor manufacturing process. The availability of real-time data has enabled prompt decision-making within the RTD application, resulting in quicker turnaround times and increased production yield. As a major telecommunications provider, the client operates numerous regional systems designed to serve local users effectively. By consolidating and integrating data from multiple sources and locations into a single centralized data repository, the client successfully established an order center that aggregates orders from various applications. Additionally, TapData integrates inventory data from over 500 stores, delivering real-time insights into stock availability and customer preferences, thereby significantly boosting supply chain efficiency. This innovative approach not only enhances operational performance but also positions the client to respond swiftly to market demands.

Apache Flink serves as a powerful framework and distributed processing engine tailored for executing stateful computations on both unbounded and bounded data streams. It has been engineered to operate seamlessly across various cluster environments, delivering computations with impressive in-memory speed and scalability. Data of all types is generated as a continuous stream of events, encompassing credit card transactions, sensor data, machine logs, and user actions on websites or mobile apps. The capabilities of Apache Flink shine particularly when handling both unbounded and bounded data sets. Its precise management of time and state allows Flink’s runtime to support a wide range of applications operating on unbounded streams. For bounded streams, Flink employs specialized algorithms and data structures optimized for fixed-size data sets, ensuring remarkable performance. Furthermore, Flink is adept at integrating with all previously mentioned resource managers, enhancing its versatility in various computing environments. This makes Flink a valuable tool for developers seeking efficient and reliable stream processing solutions.

Lura is a high-performance API Gateway framework that is extendable, simple, and stateless, suitable for both cloud-native and on-premises environments. When working with REST APIs, particularly within microservices architectures, consumers often need to interact with backend services that were not originally designed with the user interface in mind. While this approach is beneficial, it can introduce significant complexity and strain on the UI consumers due to the varying sizes and structures of microservices responses. Lura effectively serves as a bridge between clients and source servers, simplifying interactions by delivering only the essential information required by the UI. It aggregates data from multiple sources into singular endpoints, enabling users to group, wrap, transform, and condense responses as needed. Furthermore, the framework offers robust support for a wide range of middlewares and plugins, which can enhance its functionality, including the integration of Oauth authentication and additional security measures. With Lura, developers can streamline their API interactions, significantly reducing overhead for front-end applications.

Effortlessly gather, manage, and scrutinize video and data streams as they occur. Amazon Kinesis simplifies the process of collecting, processing, and analyzing streaming data in real-time, empowering you to gain insights promptly and respond swiftly to emerging information. It provides essential features that allow for cost-effective processing of streaming data at any scale while offering the adaptability to select the tools that best align with your application's needs. With Amazon Kinesis, you can capture real-time data like video, audio, application logs, website clickstreams, and IoT telemetry, facilitating machine learning, analytics, and various other applications. This service allows you to handle and analyze incoming data instantaneously, eliminating the need to wait for all data to be collected before starting the processing. Moreover, Amazon Kinesis allows for the ingestion, buffering, and real-time processing of streaming data, enabling you to extract insights in a matter of seconds or minutes, significantly reducing the time it takes compared to traditional methods. Overall, this capability revolutionizes how businesses can respond to data-driven opportunities as they arise.

Batch and streaming data processing can be streamlined effortlessly. With the capability to write once and run anywhere, it is ideal for mission-critical production tasks. Beam allows you to read data from a wide variety of sources, whether they are on-premises or cloud-based. It seamlessly executes your business logic across both batch and streaming scenarios. The outcomes of your data processing efforts can be written to the leading data sinks available in the market. This unified programming model simplifies operations for all members of your data and application teams. Apache Beam is designed for extensibility, with frameworks like TensorFlow Extended and Apache Hop leveraging its capabilities. You can run pipelines on various execution environments (runners), which provides flexibility and prevents vendor lock-in. The open and community-driven development model ensures that your applications can evolve and adapt to meet specific requirements. This adaptability makes Beam a powerful choice for organizations aiming to optimize their data processing strategies.

Navigating the challenges of your organization may seem daunting, but creating a solution doesn't have to be complicated. 3forge offers a highly adaptable, low-code platform designed to accelerate enterprise application development like never before. Need reliability? Absolutely. Looking for scalability? You got it. What about rapid deliverability? That's also guaranteed, even with intricate workflows and extensive data sets. With 3forge, you can seamlessly unify data integration, virtualization, processing, visualization, and workflows in a single solution, effectively tackling some of the most intricate real-time streaming data challenges faced today. This award-winning technology from 3forge empowers developers to launch mission-critical applications swiftly and efficiently. Discover the transformative power of real-time data coupled with zero latency, as 3forge prioritizes seamless data integration, virtualization, processing, and visualization for optimal performance. As a result, you can focus on innovation rather than getting bogged down in technical difficulties.

Lightstreamer acts as an event broker that is finely tuned for the internet, providing a smooth and instantaneous flow of data across online platforms. In contrast to conventional brokers, it adeptly manages the challenges posed by proxies, firewalls, disconnections, network congestion, and the inherent unpredictability of web connectivity. Its advanced streaming capabilities ensure that real-time data delivery is maintained, always finding efficient and reliable pathways for your information. Lightstreamer's technology is not only well-established but also at the cutting edge, continually adapting to remain a leader in the field of innovation. With a solid history and extensive practical experience, it guarantees dependable and effective data transmission. Users can count on Lightstreamer to provide unmatched reliability in any situation, making it an invaluable tool for real-time communication needs. In an ever-evolving digital landscape, Lightstreamer stands out as a trusted partner for delivering data seamlessly.