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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Apache Flink vs CDAP

Apache Flink vs CDAP

OverviewDecisionsComparisonAlternatives

Overview

CDAP
CDAP
Stacks41
Followers108
Votes0
Apache Flink
Apache Flink
Stacks534
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K

Apache Flink vs CDAP: What are the differences?

Apache Flink and CDAP are two popular data processing frameworks used for real-time data processing. In this comparison, we will highlight the key differences between Apache Flink and CDAP.

1. **Programming Model**: Apache Flink follows a DataStream API model where data is processed as a stream of events, providing low latency processing for real-time applications. CDAP, on the other hand, offers a batch processing model where data is processed in micro-batches, which is suitable for large-scale data processing.

2. **Use Cases**: Apache Flink is often preferred for real-time stream processing use cases where low latency and high throughput are critical, such as real-time analytics and monitoring. CDAP, on the other hand, is more suitable for ETL (Extract, Transform, Load) processes, batch processing, and data lake applications.

3. **Ecosystem Integration**: Apache Flink has a rich ecosystem with support for various connectors and libraries for stream processing and integration with technologies like Apache Kafka and Apache Hadoop. CDAP, on the other hand, provides integration with various storage systems, databases, and services through its plugins and extensions.

4. **Scalability**: Apache Flink is designed for horizontal scalability, allowing users to scale their processing clusters dynamically based on the workload. CDAP also supports horizontal scalability but is more focused on simplifying the development and deployment of data applications rather than large-scale processing.

5. **Resource Management**: Apache Flink comes with built-in support for resource management using Apache YARN, Apache Mesos, or Kubernetes, providing efficient cluster utilization and fault tolerance. CDAP provides resource management through its CDAP Master service, which manages the deployment and execution of data applications across the cluster.

6. **Ease of Use**: Apache Flink requires understanding of stream processing concepts and APIs, making it more suitable for developers with experience in real-time data processing. CDAP, on the other hand, provides a higher level of abstraction with visual tools and a drag-and-drop interface, making it easier for developers to create data pipelines without deep knowledge of underlying technologies.

In Summary, Apache Flink and CDAP differ in their programming models, use cases, ecosystem integration, scalability, resource management, and ease of use, making each framework more suitable for specific types of data processing applications.

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Advice on CDAP, Apache Flink

Krishna Chaitanya
Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

53.8k views53.8k
Comments
Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

CDAP
CDAP
Apache Flink
Apache Flink

Cask Data Application Platform (CDAP) is an open source application development platform for the Hadoop ecosystem that provides developers with data and application virtualization to accelerate application development, address a broader range of real-time and batch use cases, and deploy applications into production while satisfying enterprise requirements.

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

Streams for data ingestion;Reusable libraries for common Big Data access patterns;Data available to multiple applications and different paradigms;Framework level guarantees;Full development lifecycle and production deployment;Standardization of applications across programming paradigms
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars
-
GitHub Stars
25.4K
GitHub Forks
-
GitHub Forks
13.7K
Stacks
41
Stacks
534
Followers
108
Followers
879
Votes
0
Votes
38
Pros & Cons
No community feedback yet
Pros
  • 16
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
Integrations
Hadoop
Hadoop
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka

What are some alternatives to CDAP, Apache Flink?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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