Best Real-Time Data Streaming Tools for Mid Size Business - Page 2

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.

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.

Many users leverage Amazon Managed Service for Apache Flink to execute stream processing applications efficiently. This service allows for real-time transformation and analysis of streaming data utilizing Apache Flink, while seamlessly integrating with a variety of other AWS services. There is no need to manage servers and clusters, nor is there any requirement to establish computing and storage infrastructure. You only incur charges for the resources you utilize, making it cost-effective. You can build and operate Apache Flink applications without the hassle of setting up infrastructure or overseeing resources and clusters. This platform enables the processing of vast amounts of data at incredible speeds, achieving subsecond latencies, and facilitating real-time event responses. Additionally, you can deploy robust and reliable applications using Multi-AZ deployments, along with APIs designed for managing application lifecycles. Furthermore, it supports the development of applications that can transform and distribute data to various services, including Amazon Simple Storage Service (Amazon S3) and Amazon OpenSearch Service, among others. By utilizing this managed service, businesses can focus more on their applications rather than the underlying architecture.

Effortlessly capture, transform, and load live streaming data with a few simple steps. Initiate a delivery stream, pick your desired destination, and commence real-time data streaming in no time. The system autonomously provisions and adjusts compute, memory, and network capabilities without the need for continuous management. Convert unprocessed streaming data into various formats, such as Apache Parquet, and seamlessly partition the data in real-time without creating your own processing frameworks. Amazon Data Firehose stands out as the most straightforward solution for swiftly acquiring, transforming, and delivering data streams to data lakes, warehouses, and analytical platforms. To get started with Amazon Data Firehose, you need to establish a stream that includes a source, destination, and the transformations you need. The service continuously manages the data stream, automatically adapting to changes in data volume, and ensures delivery within seconds. You can choose a source for your data stream or utilize the Firehose Direct PUT API to write data directly. This makes it not only user-friendly but also highly efficient for handling large volumes of data.

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.

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.

Achieve limitless data retention for Apache Kafka® with Confluent, empowering you to be infrastructure-enabled rather than constrained by outdated systems. Traditional technologies often force a choice between real-time processing and scalability, but event streaming allows you to harness both advantages simultaneously, paving the way for innovation and success. Have you ever considered how your rideshare application effortlessly analyzes vast datasets from various sources to provide real-time estimated arrival times? Or how your credit card provider monitors millions of transactions worldwide, promptly alerting users to potential fraud? The key to these capabilities lies in event streaming. Transition to microservices and facilitate your hybrid approach with a reliable connection to the cloud. Eliminate silos to ensure compliance and enjoy continuous, real-time event delivery. The possibilities truly are limitless, and the potential for growth is unprecedented.

IBM Event Streams is a comprehensive event streaming service based on Apache Kafka, aimed at assisting businesses in managing and reacting to real-time data flows. It offers features such as machine learning integration, high availability, and secure deployment in the cloud, empowering organizations to develop smart applications that respond to events in real time. The platform is designed to accommodate multi-cloud infrastructures, disaster recovery options, and geo-replication, making it particularly suitable for critical operational tasks. By facilitating the construction and scaling of real-time, event-driven solutions, IBM Event Streams ensures that data is processed with speed and efficiency, ultimately enhancing business agility and responsiveness. As a result, organizations can harness the power of real-time data to drive innovation and improve decision-making processes.

Introducing revolutionary data streaming features that enable unparalleled customer experiences. The Kafka API and its ecosystem are fully compatible with Redpanda, which boasts predictable low latencies and ensures zero data loss. Redpanda is designed to outperform Kafka by up to ten times, offering enterprise-level support and timely hotfixes. It also includes automated backups to S3 or GCS, providing a complete escape from the routine operations associated with Kafka. Additionally, it supports both AWS and GCP environments, making it a versatile choice for various cloud platforms. Built from the ground up for ease of installation, Redpanda allows for rapid deployment of streaming services. Once you witness its incredible capabilities, you can confidently utilize its advanced features in a production setting. We take care of provisioning, monitoring, and upgrades without requiring access to your cloud credentials, ensuring that sensitive data remains within your environment. Your streaming infrastructure will be provisioned, operated, and maintained seamlessly, with customizable instance types available to suit your specific needs. As your requirements evolve, expanding your cluster is straightforward and efficient, allowing for sustainable growth.

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.

Samza enables the development of stateful applications that can handle real-time data processing from various origins, such as Apache Kafka. Proven to perform effectively at scale, it offers versatile deployment choices, allowing execution on YARN or as an independent library. With the capability to deliver remarkably low latencies and high throughput, Samza provides instantaneous data analysis. It can manage multiple terabytes of state through features like incremental checkpoints and host-affinity, ensuring efficient data handling. Additionally, Samza's operational simplicity is enhanced by its deployment flexibility—whether on YARN, Kubernetes, or in standalone mode. Users can leverage the same codebase to seamlessly process both batch and streaming data, which streamlines development efforts. Furthermore, Samza integrates with a wide range of data sources, including Kafka, HDFS, AWS Kinesis, Azure Event Hubs, key-value stores, and ElasticSearch, making it a highly adaptable tool for modern data processing needs.

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.

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.

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.

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.

Effortlessly gather, manage, and evaluate video and data streams on the fly. Amazon Kinesis simplifies the collection, processing, and analysis of streaming data in real-time, allowing you to gain valuable insights swiftly and respond to emerging information without delay. With its essential features, Amazon Kinesis provides a cost-effective way to handle streaming data at any scale while offering the adaptability to select the optimal tools tailored to your application's needs. You can utilize Amazon Kinesis to ingest real-time data types, including video, audio, application logs, website clickstreams, and IoT telemetry data, for various purposes such as machine learning and analytics. This platform allows you to process and analyze incoming data immediately, eliminating the need to wait for complete data collection before starting the processing phase. Furthermore, Amazon Kinesis supports the ingestion, buffering, and processing of streaming data instantaneously, enabling you to uncover insights within seconds or minutes instead of prolonging the wait for hours or days. The ability to act quickly based on live data enhances decision-making and operational efficiency across diverse industries.

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.

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.

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.

Apache Storm is an open-source system for distributed real-time computation that allows for the dependable processing of limitless data streams, similar to how Hadoop revolutionized batch processing. This tool is user-friendly, compatible with various programming languages, and enjoyable to work with. Its applications are diverse, including real-time analytics, continuous computation, online machine learning, distributed remote procedure calls, and extraction, transformation, and loading (ETL). Remarkably, benchmarks have shown that Apache Storm can process over a million tuples per second for each node, showcasing its speed. Additionally, it is designed to be scalable and fault-tolerant, ensuring that data processing occurs without fail while being straightforward to install and manage. Apache Storm also seamlessly integrates with existing queueing systems and database technologies. In a typical Apache Storm setup, data streams are consumed and processed through a topology that can handle complex operations, allowing for flexible repartitioning of data across different stages of computation. For a comprehensive guide, refer to the tutorial available online. This makes Apache Storm a powerful choice for organizations looking to leverage real-time data processing capabilities.

A user-friendly, robust, and dependable framework for data processing and distribution is provided by Apache NiFi. This platform is designed to support intricate and scalable directed graphs for data routing, transformation, and system mediation tasks. Among its numerous high-level features, Apache NiFi boasts a web-based user interface that facilitates a smooth integration of design, control, feedback, and monitoring processes. It is highly configurable and designed to tolerate data loss while maintaining low latency and high throughput, with dynamic prioritization capabilities. Users can modify data flows in real-time and utilize features like back pressure and data provenance to track the data's journey from start to finish, and it is also built for extensibility. The system empowers users to create their own processors and enhances the speed of development and testing processes. Security is a key focus, with features such as SSL, SSH, HTTPS, and encrypted content among its offerings. Furthermore, it includes multi-tenant authorization and comprehensive internal policy management. NiFi comprises several web applications, including a web UI, web API, and custom UIs, which requires users to configure their mappings to the root path accordingly. This level of accessibility makes it an ideal choice for organizations looking to streamline their data workflows effectively.

Batch and streaming data processing can be accomplished most easily with a flexible approach that allows you to write once and run anywhere, making it ideal for critical production tasks. Apache Beam seamlessly pulls data from a variety of sources, whether they reside on-premises or in the cloud. It processes your business logic for both batch and streaming scenarios effectively. The outcomes of your data processing are then directed to widely-used data sinks across the industry. With a unified programming model, every member of your data and application teams can work efficiently on both batch and streaming projects. Furthermore, Apache Beam is adaptable, serving as the foundation for initiatives such as TensorFlow Extended and Apache Hop. You can execute pipelines across different environments (runners), ensuring flexibility and reducing dependency on a single solution. The development is community-driven, offering support that aids in the evolution of your applications to meet specific requirements. This collaborative approach fosters innovation and responsiveness to changing data needs.

Flume is an efficient service designed for the distributed, reliable, and accessible collection, aggregation, and movement of significant volumes of log data. Its architecture is straightforward and adaptable, built on streaming data flows that ensure robustness and fault tolerance through various reliability and recovery mechanisms. The system employs a simple and extensible data model that facilitates online analytical applications effectively. Additionally, the Apache Flume team is excited to introduce the release of Flume 1.8.0, enhancing its capabilities for handling large amounts of streaming event data seamlessly. With this update, users can expect improved performance and greater efficiency in managing their data flows.

Baidu Stream Computing (BSC) offers an efficient platform for processing streaming data in real-time, characterized by minimal latency, robust throughput, and high precision. It seamlessly integrates with Spark SQL, allowing users to execute complex business logic via straightforward SQL queries, making it user-friendly. Additionally, BSC supports comprehensive life cycle management for streaming computing tasks, ensuring effective oversight. The platform is deeply integrated with various Baidu AI Cloud storage solutions, serving as both upstream and downstream components in stream processing, including Baidu Kafka, RDS, BOS, IOT Hub, Baidu ElasticSearch, TSDB, SCS, among others. Furthermore, BSC provides extensive job monitoring capabilities, enabling users to track performance metrics and establish alert rules to safeguard their operations, thereby enhancing overall efficiency and reliability in data handling.

Scale from zero to millions of events every second with Arroyo, which is delivered as a single, streamlined binary. It can be run locally on either MacOS or Linux for development purposes and easily deployed to production using Docker or Kubernetes. Arroyo represents a revolutionary approach to stream processing, specifically designed to simplify real-time operations compared to traditional batch processing. From its inception, Arroyo has been crafted so that anyone familiar with SQL can create dependable, efficient, and accurate streaming pipelines. This empowers data scientists and engineers to develop comprehensive real-time applications, models, and dashboards without needing a dedicated team of streaming specialists. Users can perform transformations, filtering, aggregation, and joining of data streams simply by writing SQL, achieving results in under a second. Furthermore, your streaming pipelines shouldn’t trigger alerts just because Kubernetes opted to reschedule your pods. With the capability to operate in contemporary, elastic cloud environments, Arroyo is suitable for everything from basic container runtimes like Fargate to extensive, distributed systems managed by Kubernetes. This versatility makes Arroyo an ideal choice for organizations looking to optimize their streaming data processes.