Amazon Redshift Spectrum vs Apache Flink

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

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

Introduction

In this article, we will discuss the key differences between Amazon Redshift Spectrum and Apache Flink.

Scalability: Amazon Redshift Spectrum is designed to handle massive data warehouses with petabytes of data. It leverages the power of Amazon Redshift by using Redshift clusters to offload and optimize query processing. On the other hand, Apache Flink is a distributed stream and batch processing framework that provides highly scalable and fault-tolerant processing of large datasets. It is specifically designed for big data applications that require real-time processing.
Data Processing Paradigm: Amazon Redshift Spectrum follows a SQL-oriented approach for data processing. It allows users to write ANSI SQL queries to perform complex analysis on their data stored in Amazon S3. In contrast, Apache Flink provides a unified framework for batch and stream processing using a programming model called "DataStream API". It supports advanced data transformations, stateful processing, and event-time-based computations.
Integration with Data Sources: Amazon Redshift Spectrum seamlessly integrates with Amazon S3, allowing users to query data directly from their S3 buckets without the need to load it into Amazon Redshift clusters. It extends the querying capabilities of Amazon Redshift to include data stored in S3. On the other hand, Apache Flink supports a wide range of data sources including file systems (like HDFS and S3), messaging systems (like Kafka and RabbitMQ), and databases (like HBase and Elasticsearch).
Real-time Processing: While Amazon Redshift Spectrum focuses on running complex analytical queries on large datasets, it does not provide real-time processing capabilities. It is more suited for batch processing and ad-hoc queries. In contrast, Apache Flink is specifically designed for real-time processing of streaming data. It provides low-latency, fault-tolerant processing and supports event-time semantics for accurate window aggregations.
Storage Model: Amazon Redshift Spectrum uses a columnar storage format for efficient data compression and query performance. It stores data in S3 in optimized columnar formats like Apache Parquet and ORC. Apache Flink, on the other hand, does not define a specific storage format. It can work with various formats including Avro, JSON, CSV, and more. Flink enables data transformations and computations without the need for a specific storage format.
Ecosystem Integration: Amazon Redshift Spectrum is tightly integrated with the AWS ecosystem, including other AWS services like Amazon S3 and AWS Glue. It provides seamless integration with AWS data processing services like AWS Lambda for serverless computing. Apache Flink, on the other hand, has a broader ecosystem integration. It can integrate with multiple data processing frameworks like Apache Kafka for data ingestion, Apache Hadoop for storage, and Apache Hive for metadata management.

In Summary, Amazon Redshift Spectrum focuses on scalable SQL-oriented data analytics on massive data warehouses, while Apache Flink provides a unified framework for real-time stream and batch processing with broader ecosystem integration capabilities.

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Advice on Apache Flink, Amazon Redshift Spectrum

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

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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Detailed Comparison

Apache Flink	Amazon Redshift Spectrum
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.	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.
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 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 99
Followers 879	Followers 147
Votes 38	Votes 3
Pros & Cons
Pros 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	Pros 1 Good Performance 1 Economical 1 Great Documentation
Integrations
YARN Hadoop Hadoop HBase Kafka	Amazon S3 Amazon Redshift

What are some alternatives to Apache Flink, Amazon Redshift Spectrum?

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

Distributed SQL Query Engine for Big 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.

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 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™ 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

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

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

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

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.

Related Comparisons

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

Introduction

In this article, we will discuss the key differences between Amazon Redshift Spectrum and Apache Flink.

Scalability: Amazon Redshift Spectrum is designed to handle massive data warehouses with petabytes of data. It leverages the power of Amazon Redshift by using Redshift clusters to offload and optimize query processing. On the other hand, Apache Flink is a distributed stream and batch processing framework that provides highly scalable and fault-tolerant processing of large datasets. It is specifically designed for big data applications that require real-time processing.
Data Processing Paradigm: Amazon Redshift Spectrum follows a SQL-oriented approach for data processing. It allows users to write ANSI SQL queries to perform complex analysis on their data stored in Amazon S3. In contrast, Apache Flink provides a unified framework for batch and stream processing using a programming model called "DataStream API". It supports advanced data transformations, stateful processing, and event-time-based computations.
Integration with Data Sources: Amazon Redshift Spectrum seamlessly integrates with Amazon S3, allowing users to query data directly from their S3 buckets without the need to load it into Amazon Redshift clusters. It extends the querying capabilities of Amazon Redshift to include data stored in S3. On the other hand, Apache Flink supports a wide range of data sources including file systems (like HDFS and S3), messaging systems (like Kafka and RabbitMQ), and databases (like HBase and Elasticsearch).
Real-time Processing: While Amazon Redshift Spectrum focuses on running complex analytical queries on large datasets, it does not provide real-time processing capabilities. It is more suited for batch processing and ad-hoc queries. In contrast, Apache Flink is specifically designed for real-time processing of streaming data. It provides low-latency, fault-tolerant processing and supports event-time semantics for accurate window aggregations.
Storage Model: Amazon Redshift Spectrum uses a columnar storage format for efficient data compression and query performance. It stores data in S3 in optimized columnar formats like Apache Parquet and ORC. Apache Flink, on the other hand, does not define a specific storage format. It can work with various formats including Avro, JSON, CSV, and more. Flink enables data transformations and computations without the need for a specific storage format.
Ecosystem Integration: Amazon Redshift Spectrum is tightly integrated with the AWS ecosystem, including other AWS services like Amazon S3 and AWS Glue. It provides seamless integration with AWS data processing services like AWS Lambda for serverless computing. Apache Flink, on the other hand, has a broader ecosystem integration. It can integrate with multiple data processing frameworks like Apache Kafka for data ingestion, Apache Hadoop for storage, and Apache Hive for metadata management.

Amazon Redshift Spectrum vs Apache Flink

Overview

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