Need advice about which tool to choose?Ask the StackShare community!
Apache Flink vs CDAP vs Pachyderm: What are the differences?
### Key Differences between Apache Flink, CDAP, and Pachyderm
Apache Flink is a stream processing platform that provides low-latency data streaming and batch processing capabilities, focusing on fault tolerance and high throughput.
1. **Real-time vs. Batch Processing**: Apache Flink is known for its strong real-time processing capabilities, while CDAP and Pachyderm are more suitable for batch processing tasks.
2. **Data Pipeline Orchestration**: CDAP offers a comprehensive data pipeline orchestration framework with features like data ingestion, transformation, and workflow management, whereas Apache Flink focuses more on data stream processing.
3. **Containerization Support**: Pachyderm stands out for its native support for containerized data pipelines, making it easy to deploy and manage data processing jobs within containerized environments.
4. **Workflow Flexibility**: CDAP provides a visual interface for designing and managing complex data workflows, offering a higher level of abstraction compared to Apache Flink and Pachyderm.
5. **Community Ecosystem**: Apache Flink has a large and active community contributing to its development and offering a wide range of integrations and extensions, providing users with a rich ecosystem of tools and libraries.
6. **Data Versioning and Lineage Tracking**: Pachyderm excels in data versioning and lineage tracking, allowing users to trace the history of their data transformations and easily revert to previous versions, a feature not extensively supported in Apache Flink and CDAP.
In Summary, Apache Flink emphasizes real-time processing, while CDAP offers comprehensive data pipeline orchestration, and Pachyderm excels in containerized data pipeline management along with robust features for data versioning and lineage tracking.
Advice on CDAP, Apache Flink, and Pachyderm
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 565K views
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.
Replies (2)
lvhuyen
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 400.1K views
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
Manage your open source components, licenses, and vulnerabilities
Learn MorePros of CDAP
Pros of Apache Flink
Pros of Pachyderm
Pros of CDAP
Be the first to leave a pro
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Pros of Pachyderm
- Containers3
- Versioning1
- Can run on GCP or AWS1
Sign up to add or upvote prosMake informed product decisions
Cons of CDAP
Cons of Apache Flink
Cons of Pachyderm
Cons of CDAP
Be the first to leave a con
Cons of Apache Flink
Be the first to leave a con
Cons of Pachyderm
- Recently acquired by HPE, uncertain future.1
Sign up to add or upvote consMake informed product decisions
42
138
1.9K
1
18
19
6
- No public GitHub repository available -
- No public GitHub repository available -
What is CDAP?
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.
What is Apache Flink?
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.
What is Pachyderm?
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
Need advice about which tool to choose?Ask the StackShare community!
Jobs that mention CDAP, Apache Flink, and Pachyderm as a desired skillset
What companies use CDAP?
What companies use Apache Flink?
What companies use Pachyderm?
What companies use Apache Flink?
What companies use Pachyderm?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with CDAP?
What tools integrate with Apache Flink?
What tools integrate with Pachyderm?
What tools integrate with CDAP?
What tools integrate with Apache Flink?
What tools integrate with Pachyderm?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+6 5
712
+4 4
1322
+7 3
2254
+8 5
2583
What are some alternatives to CDAP, Apache Flink, and Pachyderm?
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
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.
Akutan
A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
StreamSets
An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.