Azure Data Factory vs Delta Lake

Overview

Azure Data Factory

Stacks253

Followers484

Votes0

GitHub Stars516

Forks610

Delta Lake

Stacks105

Followers315

Votes0

GitHub Stars8.4K

Forks1.9K

Azure Data Factory vs Delta Lake: What are the differences?

Introduction

Azure Data Factory and Delta Lake are two popular tools in the field of data processing and management. However, they have some key differences that set them apart from each other. In this article, we will explore these differences and understand how they impact the data workflow and architecture.

Data Processing Paradigm: Azure Data Factory (ADF) is a cloud-based data integration service that orchestrates and manages the movement and transformation of data from various sources to a data lake or a data warehouse. It offers a code-free environment for building data pipelines using a visual interface. On the other hand, Delta Lake is an open-source storage layer that sits on top of a data lake and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions and data reliability. It adds additional functionalities like schema enforcement and data versioning to the existing data lake architecture.
Data Management Capabilities: Azure Data Factory provides extensive data management capabilities, including data ingestion, data transformation, and data orchestration. It allows users to consume data from various sources, integrate it with other data, and perform complex transformations before loading it into a target system. Delta Lake, on the other hand, focuses on data management within the data lake by providing data quality checks, schema enforcement, and change data capture functionalities. It enables users to have better control over data consistency and reliability within the data lake environment.
Scalability and Performance: Azure Data Factory leverages the scalability and performance of the underlying data storage and processing services in the cloud, such as Azure Blob Storage and Azure Databricks. It can efficiently handle large volumes of data and scale up or down based on the demand. Delta Lake, on the other hand, provides optimizations for data processing and querying within the data lake. It uses a file-based architecture that allows for faster data access and improved performance compared to traditional data lakes.
Data Transformation Capabilities: Azure Data Factory offers a wide range of built-in data transformation activities, such as data mapping, data conversion, and data aggregation. It supports various data manipulation techniques and allows users to define complex data transformation logic using expressions and functions. Delta Lake, on the other hand, focuses more on data management rather than data transformation. It provides support for data schema evolution, which allows users to evolve the schema of the data lake over time without breaking the existing data pipelines.
Integration with Ecosystem: Azure Data Factory integrates with a wide range of services in the Azure ecosystem, including Azure Logic Apps, Azure Functions, and Azure Machine Learning. It provides seamless connectivity between these services and enables users to build end-to-end data workflows. Delta Lake, on the other hand, can be used with various data processing engines, including Apache Spark and Presto. It provides a unified data lake experience and allows users to leverage the power of these processing engines for data analysis and exploration.
Data Governance and Security: Azure Data Factory provides robust data governance and security features, including data masking, data encryption, and role-based access control. It ensures that sensitive data is protected and meets the compliance requirements of different industries. Delta Lake, on the other hand, offers data lineage and audit capabilities to track changes made to the data lake. It provides an immutable audit log and allows users to trace back the changes made to the data for regulatory and compliance purposes.

In Summary, Azure Data Factory is a cloud-based data integration service that focuses on data movement and transformation, while Delta Lake is an open-source storage layer that provides ACID transactions and data reliability within a data lake. Both tools have different focuses and functionalities, but can be used together to build robust and scalable data processing and management solutions.

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Advice on Azure Data Factory, Delta Lake

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	Delta Lake
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.	An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	ACID Transactions; Scalable Metadata Handling; Time Travel (data versioning); Open Format; Unified Batch and Streaming Source and Sink; Schema Enforcement; Schema Evolution; 100% Compatible with Apache Spark API
Statistics
GitHub Stars 516	GitHub Stars 8.4K
GitHub Forks 610	GitHub Forks 1.9K
Stacks 253	Stacks 105
Followers 484	Followers 315
Votes 0	Votes 0
Integrations
Octotree Java .NET	Apache Spark Hadoop Amazon S3

What are some alternatives to Azure Data Factory, Delta Lake?

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.

Apache Camel

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

Splunk

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

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.

Related Comparisons

Azure Data Factory vs Delta Lake: What are the differences?

Introduction

Data Processing Paradigm: Azure Data Factory (ADF) is a cloud-based data integration service that orchestrates and manages the movement and transformation of data from various sources to a data lake or a data warehouse. It offers a code-free environment for building data pipelines using a visual interface. On the other hand, Delta Lake is an open-source storage layer that sits on top of a data lake and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions and data reliability. It adds additional functionalities like schema enforcement and data versioning to the existing data lake architecture.
Data Management Capabilities: Azure Data Factory provides extensive data management capabilities, including data ingestion, data transformation, and data orchestration. It allows users to consume data from various sources, integrate it with other data, and perform complex transformations before loading it into a target system. Delta Lake, on the other hand, focuses on data management within the data lake by providing data quality checks, schema enforcement, and change data capture functionalities. It enables users to have better control over data consistency and reliability within the data lake environment.
Scalability and Performance: Azure Data Factory leverages the scalability and performance of the underlying data storage and processing services in the cloud, such as Azure Blob Storage and Azure Databricks. It can efficiently handle large volumes of data and scale up or down based on the demand. Delta Lake, on the other hand, provides optimizations for data processing and querying within the data lake. It uses a file-based architecture that allows for faster data access and improved performance compared to traditional data lakes.
Data Transformation Capabilities: Azure Data Factory offers a wide range of built-in data transformation activities, such as data mapping, data conversion, and data aggregation. It supports various data manipulation techniques and allows users to define complex data transformation logic using expressions and functions. Delta Lake, on the other hand, focuses more on data management rather than data transformation. It provides support for data schema evolution, which allows users to evolve the schema of the data lake over time without breaking the existing data pipelines.
Integration with Ecosystem: Azure Data Factory integrates with a wide range of services in the Azure ecosystem, including Azure Logic Apps, Azure Functions, and Azure Machine Learning. It provides seamless connectivity between these services and enables users to build end-to-end data workflows. Delta Lake, on the other hand, can be used with various data processing engines, including Apache Spark and Presto. It provides a unified data lake experience and allows users to leverage the power of these processing engines for data analysis and exploration.
Data Governance and Security: Azure Data Factory provides robust data governance and security features, including data masking, data encryption, and role-based access control. It ensures that sensitive data is protected and meets the compliance requirements of different industries. Delta Lake, on the other hand, offers data lineage and audit capabilities to track changes made to the data lake. It provides an immutable audit log and allows users to trace back the changes made to the data for regulatory and compliance purposes.

Azure Data Factory vs Delta Lake

Overview

Azure Data Factory vs Delta Lake: What are the differences?