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  4. Big Data As A Service
  5. Amazon Redshift Spectrum vs Azure HDInsight

Amazon Redshift Spectrum vs Azure HDInsight

OverviewComparisonAlternatives

Overview

Azure HDInsight
Azure HDInsight
Stacks29
Followers138
Votes0
Amazon Redshift Spectrum
Amazon Redshift Spectrum
Stacks99
Followers147
Votes3

Amazon Redshift Spectrum vs Azure HDInsight: What are the differences?

Amazon Redshift Spectrum and Azure HDInsight are two popular cloud services that offer data analytics and processing capabilities. Below are some key differences between the two platforms:

  1. Integration with Data Warehouses: Amazon Redshift Spectrum is an extension of Amazon Redshift, allowing users to run queries against data stored in Amazon S3 without having to load or transform it. On the other hand, Azure HDInsight is a fully managed cloud service that uses open-source frameworks like Hadoop, Spark, and Hive to process big data. It offers more flexibility in terms of data sources and processing engines.

  2. Pricing Model: Amazon Redshift Spectrum uses a pay-as-you-go pricing model based on the amount of data scanned by queries. In contrast, Azure HDInsight has a pricing structure that includes costs for the virtual machines, storage, and data processing engines used. Users need to carefully consider their usage patterns to choose the most cost-effective option.

  3. Data Processing Engines: While both services support SQL-based querying, the underlying data processing engines differ. Amazon Redshift Spectrum uses Amazon Redshift's query processing engine for on-demand analysis of S3 data, whereas Azure HDInsight supports various processing engines like Apache Hadoop, Apache Spark, and Apache Hive for diverse big data processing tasks.

  4. Scalability: Amazon Redshift Spectrum offers automatic scaling capabilities to handle varying workloads, enabling users to adjust compute resources based on demand. Azure HDInsight also provides scalability options, allowing users to scale out clusters to accommodate increased data processing needs.

  5. Integration with Ecosystem: Amazon Redshift Spectrum is tightly integrated with AWS services like S3, Glue, and Redshift, providing a seamless data analytics solution within the AWS ecosystem. On the other hand, Azure HDInsight integrates well with other Azure services, allowing users to build end-to-end data pipelines and leverage additional Azure functionalities.

  6. Ecosystem Support: In terms of ecosystem support, Amazon Redshift Spectrum is primarily designed for AWS cloud environment, offering extensive compatibility with other AWS services. Meanwhile, Azure HDInsight is built within the Microsoft Azure ecosystem, leveraging tools and services specific to Azure cloud, providing a seamless experience for Azure users.

In Summary, Amazon Redshift Spectrum and Azure HDInsight differ in their integration with data warehouses, pricing models, data processing engines, scalability options, ecosystem integrations, and ecosystem support.

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Detailed Comparison

Azure HDInsight
Azure HDInsight
Amazon Redshift Spectrum
Amazon Redshift Spectrum

It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.

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.

Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
-
Statistics
Stacks
29
Stacks
99
Followers
138
Followers
147
Votes
0
Votes
3
Pros & Cons
No community feedback yet
Pros
  • 1
    Economical
  • 1
    Great Documentation
  • 1
    Good Performance
Integrations
IntelliJ IDEA
IntelliJ IDEA
Apache Spark
Apache Spark
Kafka
Kafka
Visual Studio Code
Visual Studio Code
Hadoop
Hadoop
Apache Storm
Apache Storm
HBase
HBase
Apache Hive
Apache Hive
Azure Data Factory
Azure Data Factory
Azure Active Directory
Azure Active Directory
Amazon S3
Amazon S3
Amazon Redshift
Amazon Redshift

What are some alternatives to Azure HDInsight, Amazon Redshift Spectrum?

Google BigQuery

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

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.

Amazon Redshift

Amazon Redshift

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.

Qubole

Qubole

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

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

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

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.

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