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Delta Lake

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Delta Lake vs Apache Flink: What are the differences?

Developers describe Delta Lake as "Reliable Data Lakes at Scale". An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. On the other hand, Apache Flink is detailed as "Fast and reliable large-scale data processing engine". 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.

Delta Lake and Apache Flink belong to "Big Data Tools" category of the tech stack.

Some of the features offered by Delta Lake are:

  • ACID Transactions
  • Scalable Metadata Handling
  • Time Travel (data versioning)

On the other hand, Apache Flink provides the following key features:

  • 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

Delta Lake and Apache Flink are both open source tools. Apache Flink with 9.36K GitHub stars and 5.01K forks on GitHub appears to be more popular than Delta Lake with 1.26K GitHub stars and 210 GitHub forks.

Advice on Delta Lake and Apache Flink
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 402.1K 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.

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Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. 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:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. 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
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. 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.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 266.8K views
Recommends
on
Apache SparkApache Spark

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"

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Pros of Delta Lake
Pros of Apache Flink
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    • 16
      Unified batch and stream processing
    • 8
      Easy to use streaming apis
    • 8
      Out-of-the box connector to kinesis,s3,hdfs
    • 4
      Open Source
    • 2
      Low latency

    Sign up to add or upvote prosMake informed product decisions

    What is Delta Lake?

    An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

    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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Delta Lake?
    What companies use Apache Flink?
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    What tools integrate with Apache Flink?

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    Blog Posts

    Mar 24 2021 at 12:57PM

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    What are some alternatives to Delta Lake and Apache Flink?
    Snowflake
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
    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.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    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.
    Apache Hive
    Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
    See all alternatives