Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.
Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.
Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.
Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.
Apache Flink Features
- Distributed execution of streaming programs on clusters of computers
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Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
- Streaming SQL query engine with support for windowing functions
- Low latency query execution in milliseconds
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Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
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Powerful API that supports both batch and streaming applications
- Runs on clusters of commodity hardware with minimal configuration
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Can be integrated with other technologies, such as Apache Spark for complex data mining
Apache Flink Benefits
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Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.
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Fault tolerance: Flink can automatically detect and recover from failures in the system.
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Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.
Reviews from Real Users
Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:
The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”
Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”
Cloudera DataFlow (CDF) is a comprehensive edge-to-cloud real-time streaming data platform that gathers, curates, and analyzes data to provide customers with useful insight for immediately actionable intelligence. It resolves issues with real-time stream processing, streaming analytics, data provenance, and data ingestion from IoT devices and other sources that are associated with data in motion. Cloudera DataFlow enables secure and controlled data intake, data transformation, and content routing because it is built entirely on open-source technologies. With regard to all of your strategic digital projects, Cloudera DataFlow enables you to provide a superior customer experience, increase operational effectiveness, and maintain a competitive edge.
With Cloudera DataFlow, you can take the next step in modernizing your data streams by connecting your on-premises flow management, streams messaging, and stream processing and analytics capabilities to the public cloud.
Cloudera DataFlow Advantage Features
Cloudera DataFlow has many valuable key features. Some of the most useful ones include:
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Edge and flow management: Edge agents and an edge management hub work together to provide the edge management capability. Edge agents can be managed, controlled, and watched over in order to gather information from edge hardware and push intelligence back to the edge. Thousands of edge devices can now be used to design, deploy, run, and monitor edge flow apps. Edge Flow Manager (EFM) is an agent management hub that enables the development, deployment, and monitoring of edge flows on thousands of MiNiFi agents using a graphical flow-based programming model.
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Streams messaging: The CDF platform guarantees that all ingested data streams can be temporarily buffered so that other applications can use the data as needed. This makes it possible for a business to scale efficiently, as data streams from thousands of origination points start to grow to petabyte sizes. To achieve IoT-scale, streams messaging allows you to buffer large data streams using a publish-subscribe strategy.
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Stream analytics and processing: The third tenet of the CDF platform is its capacity to analyze incoming data streams in real time and with minimal latency, providing actionable intelligence in the form of predictive and prescriptive insights. This stage is essential to completing the Data-in-Motion lifecycle for an enterprise because there is only a use in absorbing all real-time streams if something useful is done with them in the moment to benefit your company.
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Shared Data Experience (SDX): The most crucial component that transforms CDF into a genuine platform is Cloudera Data Platform's SDX. It is a powerful data fabric that offers the broadest possible deployment flexibility and guarantees total security, governance, and control across infrastructures. You get a single experience for security (with Apache Ranger), governance (with Apache Atlas), and data lineage from edge to cloud because all the CDF components seamlessly connect with SDX.
Cloudera DataFlow Advantage Benefits
There are many benefits to implementing Cloudera DataFlow . Some of the biggest advantages the solution offers include:
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Completely open source: Invest in your architecture with confidence, knowing that there will be no vendor lock-in.
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More than 300 pre-built processors: This is the only product that provides edge-to-cloud connection this comprehensive as well as a no-code user experience
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Integrated data provenance: The market's only platform that offers out-of-the-box, end-to-end data lineage tracking and provenance across MiNiFi, NiFi, Kafka, Flink, and more.
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Multiple stream processing engines to choose from: Supports Spark structured streaming, Kafka Streams, and Apache Flink for real-time insights and predictive analytics.
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Hundred of Kafka consumers: Cloudera has hundreds of satisfied customers who receive exceptional support for their complex Kafka implementations.
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Use cases for edge IoT: IoT data from thousands of endpoints may be easily collected, processed, and managed from the edge to the cloud with a multi-cloud/hybrid cloud strategy.
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Hybrid/multi-cloud approach: Choose a flexible deployment option for your streaming architecture that spans across edge, on-premises, and various cloud environments with ease thanks to the power of CDP.
