Amazon Redshift Spectrum vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

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

Introduction

Here, we will discuss the key differences between Amazon Redshift Spectrum and Apache Spark in terms of their functionalities and capabilities.

Data Processing Paradigm: Amazon Redshift Spectrum is a fully managed data lake query service that allows querying data directly from an Amazon S3 data lake. It leverages the massive parallel processing (MPP) architecture of Amazon Redshift to optimize and execute queries efficiently on large volumes of data stored in S3. On the other hand, Apache Spark is an open-source distributed data processing framework that enables processing and analyzing large datasets in a distributed and fault-tolerant manner. It provides a unified programming model and supports various data processing operations like batch processing, streaming, machine learning, and graph processing.
Data Storage: In Redshift Spectrum, the data is stored in Amazon S3, which allows for cost-effective storage of large volumes of data without the need for managing infrastructure. Spark, on the other hand, can work with various storage systems like Hadoop Distributed File System (HDFS), Amazon S3, Apache Cassandra, and more. It offers flexibility in terms of choosing the storage system that best suits the requirements.
Query Optimization: Redshift Spectrum leverages the MPP architecture of Amazon Redshift to execute queries efficiently by distributing the workload across multiple compute resources. It optimizes query plans based on data statistics to minimize the amount of data scanned from S3. In contrast, Apache Spark optimizes query execution using a cost-based optimizer that considers factors like data distribution, join types, and transformations. It also provides support for data caching and lazy evaluation to optimize query performance.
Data Processing Capabilities: Redshift Spectrum supports SQL queries and is optimized for querying structured and semi-structured data stored in S3. It provides advanced analytical functions and supports complex aggregations, window functions, and data type conversions. Spark, on the other hand, offers a wide range of data processing capabilities, including SQL queries, batch processing, real-time stream processing, machine learning, and graph processing. It provides a rich set of APIs and libraries to perform various data processing tasks.
Processing Speed: Redshift Spectrum is designed for high-performance query execution on large datasets stored in S3. It utilizes the distributed computing power of Amazon Redshift to achieve fast query performance. Spark, on the other hand, provides in-memory processing capability, which can significantly improve the processing speed for iterative algorithms and interactive queries. Moreover, Spark allows for data parallelism and can scale horizontally by adding more worker nodes to the cluster.
Integration with Other Ecosystems: Redshift Spectrum integrates seamlessly with other services in the AWS ecosystem, such as Amazon Redshift, AWS Glue, AWS Lambda, and Amazon Athena. It enables data movement, data transformation, and serverless query processing across these services. Spark also offers integration with a wide range of data sources and systems, including Hadoop, Hive, Kafka, Cassandra, and more. It can easily interoperate with various tools and frameworks within the big data ecosystem.

In summary, Amazon Redshift Spectrum is optimized for querying data stored in S3 using SQL and leverages the MPP architecture of Amazon Redshift. Apache Spark, on the other hand, is a distributed data processing framework that supports various data processing operations and provides in-memory processing capability. Both offer different strengths and can be chosen based on the specific requirements of the use case.

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Advice on Apache Spark, 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 Spark	Amazon Redshift Spectrum
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 99
Followers 3.5K	Followers 147
Votes 140	Votes 3
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 1 Great Documentation 1 Economical 1 Good Performance
Integrations
No integrations available	Amazon S3 Amazon Redshift

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

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.

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.

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 Spark: What are the differences?

Introduction

Here, we will discuss the key differences between Amazon Redshift Spectrum and Apache Spark in terms of their functionalities and capabilities.

Data Processing Paradigm: Amazon Redshift Spectrum is a fully managed data lake query service that allows querying data directly from an Amazon S3 data lake. It leverages the massive parallel processing (MPP) architecture of Amazon Redshift to optimize and execute queries efficiently on large volumes of data stored in S3. On the other hand, Apache Spark is an open-source distributed data processing framework that enables processing and analyzing large datasets in a distributed and fault-tolerant manner. It provides a unified programming model and supports various data processing operations like batch processing, streaming, machine learning, and graph processing.
Data Storage: In Redshift Spectrum, the data is stored in Amazon S3, which allows for cost-effective storage of large volumes of data without the need for managing infrastructure. Spark, on the other hand, can work with various storage systems like Hadoop Distributed File System (HDFS), Amazon S3, Apache Cassandra, and more. It offers flexibility in terms of choosing the storage system that best suits the requirements.
Query Optimization: Redshift Spectrum leverages the MPP architecture of Amazon Redshift to execute queries efficiently by distributing the workload across multiple compute resources. It optimizes query plans based on data statistics to minimize the amount of data scanned from S3. In contrast, Apache Spark optimizes query execution using a cost-based optimizer that considers factors like data distribution, join types, and transformations. It also provides support for data caching and lazy evaluation to optimize query performance.
Data Processing Capabilities: Redshift Spectrum supports SQL queries and is optimized for querying structured and semi-structured data stored in S3. It provides advanced analytical functions and supports complex aggregations, window functions, and data type conversions. Spark, on the other hand, offers a wide range of data processing capabilities, including SQL queries, batch processing, real-time stream processing, machine learning, and graph processing. It provides a rich set of APIs and libraries to perform various data processing tasks.
Processing Speed: Redshift Spectrum is designed for high-performance query execution on large datasets stored in S3. It utilizes the distributed computing power of Amazon Redshift to achieve fast query performance. Spark, on the other hand, provides in-memory processing capability, which can significantly improve the processing speed for iterative algorithms and interactive queries. Moreover, Spark allows for data parallelism and can scale horizontally by adding more worker nodes to the cluster.
Integration with Other Ecosystems: Redshift Spectrum integrates seamlessly with other services in the AWS ecosystem, such as Amazon Redshift, AWS Glue, AWS Lambda, and Amazon Athena. It enables data movement, data transformation, and serverless query processing across these services. Spark also offers integration with a wide range of data sources and systems, including Hadoop, Hive, Kafka, Cassandra, and more. It can easily interoperate with various tools and frameworks within the big data ecosystem.

Amazon Redshift Spectrum vs Apache Spark

Overview

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