Overview

Amazon Redshift Spectrum

Stacks99

Followers147

Votes3

AWS Glue

Stacks461

Followers819

Votes9

AWS Glue vs Amazon Redshift Spectrum: What are the differences?

Introduction

AWS Glue and Amazon Redshift Spectrum are two powerful tools offered by Amazon Web Services (AWS) that can be used for data analysis and processing. While both services provide capabilities for querying and analyzing large datasets, there are key differences between AWS Glue and Amazon Redshift Spectrum that make them suitable for different use cases.

Data Storage and Querying: One key difference between AWS Glue and Amazon Redshift Spectrum is the way they store and query data. AWS Glue is a fully managed extract, transform, and load (ETL) service that can handle structured and semi-structured data. It provides a centralized metadata repository and supports batch and real-time data processing. On the other hand, Amazon Redshift Spectrum is a feature of Amazon Redshift, a data warehousing service. Redshift Spectrum enables users to query data directly from their external data sources, such as Amazon S3, without the need to load the data into Redshift first.
Data Processing Engine: Another important difference is the underlying data processing engine used by each service. AWS Glue uses Apache Spark, a powerful open-source analytics engine, to process and transform the data. Spark provides a distributed computing model that can handle large datasets and supports a wide range of data processing tasks. In contrast, Amazon Redshift Spectrum uses a massively parallel processing (MPP) architecture to process queries on large datasets stored in Amazon S3. Redshift Spectrum leverages the same query optimizer and execution engine as Amazon Redshift, providing high-performance query processing capabilities.
Cost Structure: The cost structure of AWS Glue and Amazon Redshift Spectrum also differs. AWS Glue pricing is based on the number of Data Processing Units (DPUs) used per hour, as well as the number of crawlers, classifiers, and development endpoints provisioned. On the other hand, Amazon Redshift Spectrum pricing is based on the amount of data scanned by queries. Users are charged per terabyte of data scanned, with separate pricing for standard and Athena data formats. The cost implications of using each service should be carefully evaluated based on the specific use case and data processing requirements.
Data Transformation Capabilities: AWS Glue provides a rich set of data transformation capabilities, including data cleansing, deduplication, and normalization. These transformations can be applied during the ETL process to improve data quality and consistency. In contrast, Amazon Redshift Spectrum focuses primarily on querying and analyzing data rather than data transformation. While Redshift Spectrum provides a limited set of data manipulation functions, its main strength lies in the ability to directly query external data sources stored in Amazon S3.
Performance and Scaling: When it comes to performance and scaling, AWS Glue and Amazon Redshift Spectrum have different strengths. AWS Glue's use of Apache Spark allows for distributed processing and parallel execution, making it well-suited for handling large datasets and complex transformations. On the other hand, Amazon Redshift Spectrum's MPP architecture enables parallel query execution across multiple Redshift Spectrum nodes, providing high-performance querying capabilities. The choice between the two services depends on the specific performance and scaling requirements of the workload.
Integration with Other AWS Services: Both AWS Glue and Amazon Redshift Spectrum integrate well with other AWS services, but in different ways. AWS Glue integrates with various AWS services, such as Amazon S3, Amazon RDS, and Amazon Redshift, to facilitate data ingestion and transformation. It also supports custom connectors for connecting to on-premises data sources. On the other hand, Amazon Redshift Spectrum seamlessly integrates with Amazon Redshift, allowing users to query external data sources stored in Amazon S3 without the need for data movement or ETL.

In Summary, AWS Glue and Amazon Redshift Spectrum are two AWS services with distinct differences. AWS Glue is a fully managed ETL service that provides data processing and transformation capabilities, while Amazon Redshift Spectrum is a feature of Amazon Redshift that enables querying of data directly from external sources. The choice between the two services depends on factors such as data storage and querying requirements, data processing engine preference, cost structure, data transformation needs, performance and scaling requirements, and integration with other AWS services.

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Advice on Amazon Redshift Spectrum, AWS Glue

Aditya

Mar 13, 2021

Review

you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster

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Comments

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

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Comments

Detailed Comparison

Amazon Redshift Spectrum	AWS Glue
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.	A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
-	Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.
Statistics
Stacks 99	Stacks 461
Followers 147	Followers 819
Votes 3	Votes 9
Pros & Cons
Pros 1 Economical 1 Great Documentation 1 Good Performance	Pros 9 Managed Hive Metastore
Integrations
Amazon S3 Amazon Redshift	Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL

What are some alternatives to Amazon Redshift Spectrum, AWS Glue?

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.

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.