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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Alation vs Azure HDInsight

Alation vs Azure HDInsight

OverviewComparisonAlternatives

Overview

Azure HDInsight
Azure HDInsight
Stacks29
Followers138
Votes0
Alation
Alation
Stacks14
Followers26
Votes0

Alation vs Azure HDInsight: What are the differences?

Introduction

Here, we will compare the key differences between Alation and Azure HDInsight.

  1. Purpose and Focus: Alation is a data catalog software designed to help organizations discover, understand, and trust their data, enabling users to search, query, and collaborate on data assets. On the other hand, Azure HDInsight is a cloud-based service from Microsoft that provides fully managed Apache Hadoop, Spark, HBase, and Storm clusters for big data analytics and processing.

  2. Deployment: Alation is typically deployed on-premises or in cloud environments by the organization itself, allowing for full control over data governance and security. In contrast, Azure HDInsight is a fully managed service provided by Microsoft on the Azure cloud platform, enabling users to quickly deploy and scale big data processing clusters without the need for managing underlying infrastructure.

  3. Cost Structure: Alation follows a subscription-based pricing model where organizations pay for the software based on the number of users or data assets being managed. In comparison, Azure HDInsight offers a pay-as-you-go pricing model, allowing users to only pay for the resources they consume without any upfront costs.

  4. Ecosystem Integration: Alation integrates with a wide range of data sources and tools to provide a comprehensive data catalog experience, connecting with databases, data lakes, BI tools, and data governance platforms. Azure HDInsight, on the other hand, is part of the broader Azure ecosystem, offering seamless integration with other Azure services like Azure Data Lake Storage, Azure Databricks, and Azure Synapse Analytics.

  5. Scalability and Performance: Alation is focused on data discovery and collaboration, facilitating data governance and knowledge sharing within organizations. Azure HDInsight, being a big data processing platform, is designed for high scalability and performance, allowing users to process large volumes of data using distributed computing technologies like Hadoop and Spark.

  6. Data Processing Capabilities: While Alation primarily focuses on metadata management and data governance, Azure HDInsight provides a wide range of big data processing capabilities, including batch processing, real-time analytics, interactive querying, and machine learning, making it suitable for a variety of data processing tasks.

In Summary, Alation focuses on data catalog and governance, while Azure HDInsight is a cloud-based big data processing service with managed Hadoop, Spark, HBase, and Storm clusters.

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Detailed Comparison

Azure HDInsight
Azure HDInsight
Alation
Alation

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

The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights.

Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises
Data Catalog; Automatically indexes your data by source; Automatically gathers knowledge about your data
Statistics
Stacks
29
Stacks
14
Followers
138
Followers
26
Votes
0
Votes
0
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
No integrations available

What are some alternatives to Azure HDInsight, Alation?

Segment

Segment

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

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.

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