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  1. Stackups
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  5. Amazon Redshift Spectrum vs Azure Data Factory

Amazon Redshift Spectrum vs Azure Data Factory

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks254
Followers484
Votes0
GitHub Stars516
Forks610
Amazon Redshift Spectrum
Amazon Redshift Spectrum
Stacks99
Followers147
Votes3

Amazon Redshift Spectrum vs Azure Data Factory: What are the differences?

Introduction

In this Markdown document, we will compare Amazon Redshift Spectrum and Azure Data Factory. These two cloud-based services provide solutions for big data and analytics, but they have key differences that set them apart. Below we will discuss these differences in detail.

  1. Data Warehouse vs Data Integration: Amazon Redshift Spectrum is primarily a data warehousing service that allows users to query large amounts of structured and semi-structured data stored in Amazon S3. It extends the capabilities of Amazon Redshift by enabling on-demand access to data outside of the Redshift cluster. On the other hand, Azure Data Factory is a data integration service that allows users to create data-driven workflows for orchestrating and automating data movement and data transformation processes across various data sources and destinations.

  2. Native Query Engine vs Orchestration and Transformation: Redshift Spectrum leverages the advanced query engine of Amazon Redshift, which is based on PostgreSQL, to seamlessly query data stored in Amazon S3. It allows users to perform complex analytics and aggregations on vast amounts of data. In contrast, Azure Data Factory focuses on orchestrating data movement and transformation workflows, rather than providing a native query engine. It can leverage various data processing engines, such as Azure Databricks or Azure HDInsight, for performing data transformations.

  3. Serverless vs Provisioned: Redshift Spectrum operates on a serverless architecture, which means users do not need to provision or manage any infrastructure. The service scales automatically to accommodate the workload and offers cost-efficient pricing based on the amount of data scanned during queries. Azure Data Factory, on the other hand, requires users to provision and manage the required infrastructure for data movement and transformation workflows. Users have more control over resource allocation but are responsible for scaling and optimizing the infrastructure.

  4. Integration with AWS Ecosystem vs Integration with Azure Services: Redshift Spectrum seamlessly integrates with various AWS services, including Amazon S3, Amazon Athena, and AWS Glue. It allows users to leverage their existing data and tools within the AWS ecosystem. Azure Data Factory, as an integral part of the Azure ecosystem, provides deep integration with other Azure services, such as Azure Blob Storage, Azure SQL Database, and Azure Data Lake Storage. This integration allows users to utilize the full potential of the Azure platform for their data workflows.

  5. Data Partitioning and Compression: Redshift Spectrum offers features like data partitioning and compression to improve query performance and reduce costs. Users can partition their data based on specific columns, which enables queries to skip unnecessary data scans. Additionally, data compression techniques like columnar storage and automatic compression help reduce storage costs and increase query speed. Azure Data Factory does not provide built-in data partitioning or compression features. Users need to implement these optimizations manually if required.

  6. Cost Structure: Redshift Spectrum follows a pricing model based on the amount of data scanned during queries. Users are billed per terabyte of data scanned, making it suitable for sporadic or ad-hoc analysis over large datasets. On the other hand, Azure Data Factory pricing is based on a combination of data movement, data transformation, and orchestration activities. Users pay for the number of activity runs and data movement operations, which makes it suitable for regular data integration and transformation workflows.

In summary, Redshift Spectrum is a serverless, data warehousing service with native query capabilities, while Azure Data Factory is a data integration service focused on orchestrating data movement and transformation workflows within the Azure ecosystem. Redshift Spectrum seamlessly integrates with AWS services, provides data partitioning and compression features, and follows a pricing model based on data scanned. Azure Data Factory provides deep integration with Azure services, requires provisioning of infrastructure, and offers a pricing model based on activity runs and data movement operations.

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Advice on Azure Data Factory, Amazon Redshift Spectrum

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

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

Detailed Comparison

Azure Data Factory
Azure Data Factory
Amazon Redshift Spectrum
Amazon Redshift Spectrum

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
-
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
254
Stacks
99
Followers
484
Followers
147
Votes
0
Votes
3
Pros & Cons
No community feedback yet
Pros
  • 1
    Economical
  • 1
    Great Documentation
  • 1
    Good Performance
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
Amazon S3
Amazon S3
Amazon Redshift
Amazon Redshift

What are some alternatives to Azure Data Factory, Amazon Redshift Spectrum?

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

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

Apache Impala

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.

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