Amazon Redshift Spectrum vs Apache Spark

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

Amazon Redshift Spectrum

99
139
+ 1
3
Apache Spark

2.7K
3.1K
+ 1
137
Add tool

Amazon Redshift Spectrum vs Apache Spark: What are the differences?

What is Amazon Redshift Spectrum? Exabyte-Scale In-Place Queries of S3 Data. With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.

What is Apache Spark? Fast and general engine for large-scale data processing. 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.

Amazon Redshift Spectrum and Apache Spark can be primarily classified as "Big Data" tools.

Apache Spark is an open source tool with 22.5K GitHub stars and 19.4K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.

According to the StackShare community, Apache Spark has a broader approval, being mentioned in 266 company stacks & 112 developers stacks; compared to Amazon Redshift Spectrum, which is listed in 5 company stacks and 4 developer stacks.

Advice on Amazon Redshift Spectrum and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 311.3K 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.

See more
Replies (2)
Recommends
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.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 200.4K views
Recommends
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"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon Redshift Spectrum
Pros of Apache Spark
  • 1
    Good Performance
  • 1
    Great Documentation
  • 1
    Economical
  • 59
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 7
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Redshift Spectrum
Cons of Apache Spark
    Be the first to leave a con
    • 3
      Speed

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Amazon Redshift Spectrum?

    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.

    What is 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.

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

    Jobs that mention Amazon Redshift Spectrum and Apache Spark as a desired skillset
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United States of America Texas Richardson
    CBRE
    United Kingdom of Great Britain and Northern Ireland England Feltham
    CBRE
    India Telangana Hyderabad
    CBRE
    India Telangana Hyderabad
    What companies use Amazon Redshift Spectrum?
    What companies use Apache Spark?
    See which teams inside your own company are using Amazon Redshift Spectrum or Apache Spark.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon Redshift Spectrum?
    What tools integrate with Apache Spark?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Mar 24 2021 at 12:57PM

    Pinterest

    GitJenkinsKafka+7
    3
    1775
    MySQLKafkaApache Spark+6
    2
    1739
    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    6
    2271
    What are some alternatives to Amazon Redshift Spectrum and Apache Spark?
    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.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    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.
    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