Azure Data Factory vs BDS

Overview

Azure Data Factory

Stacks254

Followers484

Votes0

GitHub Stars516

Forks610

BDS

Stacks3

Followers9

Votes0

GitHub Stars996

Forks61

Azure Data Factory vs BDS: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between Azure Data Factory (ADF) and Big Data Services (BDS).

Integration Capabilities: Azure Data Factory is a cloud-based data integration service that allows data movement and orchestration between various data sources and destinations. It provides extensive integration capabilities for a wide range of data platforms and services such as Azure Blob Storage, Azure SQL Database, and Azure Databricks. On the other hand, Big Data Services (BDS) is a cloud-based managed service that enables the analysis and processing of large datasets using tools like Apache Spark and Hadoop. Unlike ADF, BDS is specifically geared towards handling big data workloads and offers advanced analytics capabilities.
Data Processing: While both ADF and BDS are data-centric services, they have different approaches to data processing. Azure Data Factory focuses on data movement and orchestration, providing a platform for data pipeline creation and management. It enables users to transform and manipulate data while moving it between different data sources and destinations. In contrast, Big Data Services is designed for complex data processing tasks and offers advanced data analytics functionalities. It supports distributed data processing frameworks like Apache Spark, which allows for large-scale data processing and analytics.
Scalability: Another key difference between ADF and BDS is in terms of scalability. Azure Data Factory is built to handle both small-scale and large-scale data integration scenarios. It can scale up or down based on the workload requirements. In comparison, Big Data Services is primarily focused on big data workloads that require high scalability and processing power. BDS can automatically scale the resources as per the workload demands, enabling users to process large volumes of data efficiently.
Managed Service: Azure Data Factory is a fully-managed service provided by Microsoft Azure, where users can focus on building data pipelines and orchestration without worrying about infrastructure management. It takes care of monitoring, scaling, and maintaining the service. On the other hand, Big Data Services is also a managed service but with a focus on big data analytics. It provides a managed environment for running Apache Spark and other big data tools, abstracting the underlying infrastructure complexities and allowing users to focus on data processing and analysis.
Ecosystem Integration: Azure Data Factory integrates well with the broader Azure ecosystem, allowing seamless connectivity and integration with various Azure services. It provides connectors for popular Azure services like Azure Storage, Azure SQL Database, and Azure Databricks. On the other hand, Big Data Services also integrates with the Azure ecosystem but with a focus on big data analytics tools and services. It provides integration with Azure Storage, Azure SQL Database, Azure Data Lake Storage, and other big data stores.
Pricing Model: The pricing models for Azure Data Factory and Big Data Services differ. Azure Data Factory follows a consumption-based pricing model where users pay for the data movement and data processing activities performed by the service. The pricing is based on usage and the number of data integration and transformation activities executed. In contrast, Big Data Services follows a similar consumption-based pricing model for data processing, but it also includes additional charges for the compute and storage resources used by the underlying big data tools like Apache Spark.

In summary, Azure Data Factory is a cloud-based data integration service with extensive integration capabilities and a focus on data movement and orchestration. On the other hand, Big Data Services is a managed service that enables the processing and analysis of large datasets using tools like Apache Spark, with a focus on big data workloads and advanced analytics.

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Advice on Azure Data Factory, BDS

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	BDS
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.	It is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.
Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing	Cover dozens of well-known Blockchain projects, including BTC, ETH, LTC, XRP, BCH, etc;Provide an interactive data visualization BI tool;Support standard SQL Query statements so that complex query logics can be implemented easily;Provide products that query data on the Blockchain in China and it also provides data visualization BI tools
Statistics
GitHub Stars 516	GitHub Stars 996
GitHub Forks 610	GitHub Forks 61
Stacks 254	Stacks 3
Followers 484	Followers 9
Votes 0	Votes 0
Integrations
Octotree Java .NET	Grafana PostgreSQL Kafka Microsoft SQL Server Confluent

What are some alternatives to Azure Data Factory, BDS?

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 BDS: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between Azure Data Factory (ADF) and Big Data Services (BDS).

Integration Capabilities: Azure Data Factory is a cloud-based data integration service that allows data movement and orchestration between various data sources and destinations. It provides extensive integration capabilities for a wide range of data platforms and services such as Azure Blob Storage, Azure SQL Database, and Azure Databricks. On the other hand, Big Data Services (BDS) is a cloud-based managed service that enables the analysis and processing of large datasets using tools like Apache Spark and Hadoop. Unlike ADF, BDS is specifically geared towards handling big data workloads and offers advanced analytics capabilities.
Data Processing: While both ADF and BDS are data-centric services, they have different approaches to data processing. Azure Data Factory focuses on data movement and orchestration, providing a platform for data pipeline creation and management. It enables users to transform and manipulate data while moving it between different data sources and destinations. In contrast, Big Data Services is designed for complex data processing tasks and offers advanced data analytics functionalities. It supports distributed data processing frameworks like Apache Spark, which allows for large-scale data processing and analytics.
Scalability: Another key difference between ADF and BDS is in terms of scalability. Azure Data Factory is built to handle both small-scale and large-scale data integration scenarios. It can scale up or down based on the workload requirements. In comparison, Big Data Services is primarily focused on big data workloads that require high scalability and processing power. BDS can automatically scale the resources as per the workload demands, enabling users to process large volumes of data efficiently.
Managed Service: Azure Data Factory is a fully-managed service provided by Microsoft Azure, where users can focus on building data pipelines and orchestration without worrying about infrastructure management. It takes care of monitoring, scaling, and maintaining the service. On the other hand, Big Data Services is also a managed service but with a focus on big data analytics. It provides a managed environment for running Apache Spark and other big data tools, abstracting the underlying infrastructure complexities and allowing users to focus on data processing and analysis.
Ecosystem Integration: Azure Data Factory integrates well with the broader Azure ecosystem, allowing seamless connectivity and integration with various Azure services. It provides connectors for popular Azure services like Azure Storage, Azure SQL Database, and Azure Databricks. On the other hand, Big Data Services also integrates with the Azure ecosystem but with a focus on big data analytics tools and services. It provides integration with Azure Storage, Azure SQL Database, Azure Data Lake Storage, and other big data stores.
Pricing Model: The pricing models for Azure Data Factory and Big Data Services differ. Azure Data Factory follows a consumption-based pricing model where users pay for the data movement and data processing activities performed by the service. The pricing is based on usage and the number of data integration and transformation activities executed. In contrast, Big Data Services follows a similar consumption-based pricing model for data processing, but it also includes additional charges for the compute and storage resources used by the underlying big data tools like Apache Spark.

Azure Data Factory vs BDS

Overview

Azure Data Factory vs BDS: What are the differences?