Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Alation

Stacks14

Followers26

Votes0

Alation vs Azure Data Factory: What are the differences?

Key Differences between Alation and Azure Data Factory

Alation and Azure Data Factory are two popular tools used in the field of data management and analytics. While both tools share some similarities in terms of data integration and data processing capabilities, there are several key differences that set them apart.

1. Storage Architecture: Alation is primarily a data catalog and metadata management tool that offers centralized storage of metadata. It provides a unified view of all data assets and metadata across various sources, enabling users to easily search, discover, and understand data. On the other hand, Azure Data Factory is a cloud-based data integration service that enables users to create data-driven workflows to ingest, prepare, transform, and publish data. It stores data in Azure Storage.

2. Data Integration and Transformation Capabilities: Azure Data Factory provides a wide range of connectors and data integration capabilities to ingest and transform data from various sources such as databases, files, and cloud services. It offers a visual interface for creating complex data workflows and transformations. Alation, on the other hand, focuses more on metadata management and data discovery rather than providing extensive data integration and transformation capabilities.

3. Workflow Orchestration and Monitoring: Azure Data Factory provides a robust workflow orchestration and monitoring mechanism. It allows users to schedule, monitor, and manage data pipelines and activities, ensuring data is processed and transformed according to defined schedules and dependencies. Alation, on the other hand, does not offer built-in workflow orchestration and monitoring features.

4. Collaboration and Governance: Alation provides collaboration features that enable users to annotate, comment on, and collaborate on data assets and queries. It also offers data governance capabilities such as data lineage and impact analysis. Azure Data Factory, on the other hand, does not provide specific collaboration features but integrates with other Azure services like Azure DevOps for collaboration and Azure Data Catalog for governance.

5. Scalability and Performance: Azure Data Factory is highly scalable and can handle large volumes of data processing and integration tasks. It leverages the scalability and computing power of Azure infrastructure, allowing users to scale up or down based on their needs. Alation, while able to handle large amounts of metadata, may not offer the same level of scalability and performance for data processing and integration tasks.

6. Pricing and Deployment: Azure Data Factory is a cloud-native service offered by Microsoft Azure and is billed based on the usage and resources consumed. It provides flexibility in terms of deployment options, allowing users to choose between different pricing tiers and regions. Alation, on the other hand, is a software solution that can be deployed on-premises or in the cloud. Its pricing structure may vary depending on the deployment option and the number of users.

In summary, Alation and Azure Data Factory differ in their primary focus, with Alation being more focused on metadata management and data discovery, while Azure Data Factory offers comprehensive data integration and transformation capabilities. Additionally, Azure Data Factory provides workflow orchestration and monitoring features, collaboration and governance capabilities, scalability and performance, and flexible pricing and deployment options, which may not be available in Alation.

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Advice on Azure Data Factory, Alation

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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Detailed Comparison

Azure Data Factory	Alation
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.	The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Data Catalog; Automatically indexes your data by source; Automatically gathers knowledge about your data
Statistics
GitHub Stars 516	GitHub Stars -
GitHub Forks 610	GitHub Forks -
Stacks 253	Stacks 14
Followers 484	Followers 26
Votes 0	Votes 0
Integrations
Octotree Java .NET	No integrations available

What are some alternatives to Azure Data Factory, Alation?

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.

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.