Amazon Redshift vs Azure Synapse

Overview

Amazon Redshift

Stacks1.5K

Followers1.4K

Votes108

Azure Synapse

Stacks104

Followers230

Votes10

Amazon Redshift vs Azure Synapse: What are the differences?

Introduction

Amazon Redshift and Azure Synapse are popular cloud-based data warehousing solutions that offer scalability, performance, and analytical capabilities. While both platforms share similarities, they also have key differences that set them apart. The following paragraphs highlight the main distinctions between Amazon Redshift and Azure Synapse.

Pricing Model: Amazon Redshift follows a pay-as-you-go pricing model, allowing users to scale their resources up or down based on their needs. On the other hand, Azure Synapse offers a consumption-based pricing model, where users pay for the resources they consume, including storage, data movement, and processing. This pricing flexibility can be advantageous for organizations with fluctuating workloads and budgets.
Integration with Ecosystem: Amazon Redshift integrates seamlessly with other AWS services, such as Amazon S3 for data storage and AWS Glue for data integration. It also has close integration with popular business intelligence tools like Tableau and Power BI. Azure Synapse, being part of the broader Azure ecosystem, offers deep integration with other Azure services like Azure Data Lake Storage and Azure Databricks for advanced analytics and data engineering workflows. The choice between the two platforms often depends on the existing cloud ecosystem of an organization.
Enhanced Analytics Capabilities: Redshift includes advanced analytics functionality, such as machine learning capabilities with integration to Amazon SageMaker. It also supports massively parallel processing (MPP) to handle complex queries efficiently. On the other hand, Azure Synapse offers integrated big data and analytical capabilities through its native Apache Spark integration. This enables users to perform distributed data processing and machine learning tasks directly within the platform, providing more comprehensive analytics capabilities.
Data Movement and Integration: Redshift provides several options for data ingestion and integration, including bulk data loading, streaming data ingestion via Kinesis Data Firehose, and direct query pushes using Redshift Spectrum. Azure Synapse, on the other hand, offers robust data integration capabilities through Azure Data Factory, enabling seamless data movement and orchestration across different Azure services and on-premises systems. The choice of platform would depend on the specific data integration requirements of an organization.
Data Lake Integration: Azure Synapse has built-in integration with Azure Data Lake Storage, which allows users to access and analyze data from data lakes seamlessly. Redshift, being primarily a data warehousing solution, requires additional configuration and setup to integrate with data lakes. This difference makes Azure Synapse a more suitable choice for organizations that heavily rely on data lake storage and want to perform analytics on data lakes directly.
Security and Governance: Both Redshift and Synapse offer robust security measures, such as encryption at rest and in transit, access controls, and integration with identity providers. However, Azure Synapse provides tighter integration with Azure Active Directory (AAD), enabling organizations to enforce centralized user management and access control policies across their Azure environment. This centralized governance capability can be advantageous for organizations with strict compliance and security requirements.

In summary, Amazon Redshift and Azure Synapse differ in their pricing models, ecosystem integration, analytics capabilities, data integration options, data lake integration, and security/governance features. Organizations should evaluate these differences and align them with their specific requirements to determine the most suitable data warehousing solution for their needs.

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Advice on Amazon Redshift, Azure Synapse

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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Detailed Comparison

Amazon Redshift	Azure Synapse
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.	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.
Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.;Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.;No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.;Fault Tolerant- Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3.;SQL - Amazon Redshift is a SQL data warehouse and uses industry standard ODBC and JDBC connections and Postgres drivers.;Isolation - Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster.;Encryption – With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-acccelerated AES-256 encryption for data at rest.<br>	Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks 1.5K	Stacks 104
Followers 1.4K	Followers 230
Votes 108	Votes 10
Pros & Cons
Pros 41 Data Warehousing 27 Scalable 17 SQL 14 Backed by Amazon 5 Encryption	Pros 4 ETL 3 Security 2 Serverless 1 Doesn't support cross database query Cons 1 Dictionary Size Limitation - CCI 1 Concurrency
Integrations
SQLite MySQL Oracle PL/SQL	No integrations available

What are some alternatives to Amazon Redshift, Azure Synapse?

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

Related Comparisons

Amazon Redshift vs Azure Synapse: What are the differences?

Introduction

Pricing Model: Amazon Redshift follows a pay-as-you-go pricing model, allowing users to scale their resources up or down based on their needs. On the other hand, Azure Synapse offers a consumption-based pricing model, where users pay for the resources they consume, including storage, data movement, and processing. This pricing flexibility can be advantageous for organizations with fluctuating workloads and budgets.
Integration with Ecosystem: Amazon Redshift integrates seamlessly with other AWS services, such as Amazon S3 for data storage and AWS Glue for data integration. It also has close integration with popular business intelligence tools like Tableau and Power BI. Azure Synapse, being part of the broader Azure ecosystem, offers deep integration with other Azure services like Azure Data Lake Storage and Azure Databricks for advanced analytics and data engineering workflows. The choice between the two platforms often depends on the existing cloud ecosystem of an organization.
Enhanced Analytics Capabilities: Redshift includes advanced analytics functionality, such as machine learning capabilities with integration to Amazon SageMaker. It also supports massively parallel processing (MPP) to handle complex queries efficiently. On the other hand, Azure Synapse offers integrated big data and analytical capabilities through its native Apache Spark integration. This enables users to perform distributed data processing and machine learning tasks directly within the platform, providing more comprehensive analytics capabilities.
Data Movement and Integration: Redshift provides several options for data ingestion and integration, including bulk data loading, streaming data ingestion via Kinesis Data Firehose, and direct query pushes using Redshift Spectrum. Azure Synapse, on the other hand, offers robust data integration capabilities through Azure Data Factory, enabling seamless data movement and orchestration across different Azure services and on-premises systems. The choice of platform would depend on the specific data integration requirements of an organization.
Data Lake Integration: Azure Synapse has built-in integration with Azure Data Lake Storage, which allows users to access and analyze data from data lakes seamlessly. Redshift, being primarily a data warehousing solution, requires additional configuration and setup to integrate with data lakes. This difference makes Azure Synapse a more suitable choice for organizations that heavily rely on data lake storage and want to perform analytics on data lakes directly.
Security and Governance: Both Redshift and Synapse offer robust security measures, such as encryption at rest and in transit, access controls, and integration with identity providers. However, Azure Synapse provides tighter integration with Azure Active Directory (AAD), enabling organizations to enforce centralized user management and access control policies across their Azure environment. This centralized governance capability can be advantageous for organizations with strict compliance and security requirements.

Amazon Redshift vs Azure Synapse

Overview

Amazon Redshift vs Azure Synapse: What are the differences?