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  1. Stackups
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  5. Azure Data Factory vs Talend

Azure Data Factory vs Talend

OverviewDecisionsComparisonAlternatives

Overview

Talend
Talend
Stacks297
Followers249
Votes0
Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610

Azure Data Factory vs Talend: What are the differences?

Introduction

In this article, we will explore the key differences between Azure Data Factory and Talend. Both Azure Data Factory and Talend are popular data integration tools that assist organizations in orchestrating and managing data workflows. However, they have distinct features and capabilities that set them apart. Let's dive into the differences between these two platforms.

1. Native Cloud Support: Azure Data Factory Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure. It offers native support for various Azure services, such as Azure Blob Storage, Azure Data Lake Storage, and Azure SQL Database. This native cloud support allows users to seamlessly integrate data across different Azure services, making it a powerful tool for building sophisticated data workflows in the cloud.

2. Broad Connectivity and Open Source: Talend Talend, on the other hand, is an open-source data integration platform that offers a wide range of connectivity options. It supports numerous data sources and targets, including popular databases, cloud storage services, and enterprise applications. Additionally, Talend provides connectors for various technologies and frameworks like Hadoop, Kafka, and Spark, enabling users to work with diverse data ecosystems.

3. Scalability and Elasticity: Azure Data Factory Azure Data Factory leverages the elasticity and scalability of the Azure cloud infrastructure to handle large-scale data integration tasks efficiently. It can automatically scale up or down based on workload demands, ensuring efficient resource utilization and cost-effectiveness. With Azure Data Factory, users can seamlessly process and move massive volumes of data across Azure services with ease.

4. Data Transformation Capabilities: Talend Talend offers advanced data transformation capabilities, allowing users to manipulate and cleanse data at various stages of the integration process. It provides a comprehensive set of built-in data transformation functions, including data type conversions, filtering, sorting, and aggregating, among others. These features enable users to transform data into the desired structure for analysis or consumption.

5. Ecosystem Integration: Azure Data Factory Azure Data Factory integrates seamlessly with other Azure services and the broader Microsoft ecosystem. It offers tight integration with Azure Machine Learning, Azure Databricks, and Power BI, enabling users to leverage these services for advanced analytics and visualization. Additionally, Azure Data Factory can orchestrate data workflows that involve on-premises data sources and hybrid cloud scenarios, making it suitable for organizations with diverse data landscapes.

6. Data Governance and Security: Talend Talend emphasizes data governance and security, offering robust features to ensure compliance and protect sensitive data. It provides data masking, encryption, and access control mechanisms to safeguard data during integration processes. Furthermore, Talend supports data lineage tracking and auditing, enabling organizations to maintain visibility and accountability for data operations.

In Summary, Azure Data Factory excels in native cloud support, scalability, and ecosystem integration, while Talend stands out with its broad connectivity, data transformation capabilities, and focus on data governance and security. Choosing between Azure Data Factory and Talend depends on specific requirements, data environment, and the level of flexibility and control needed in the data integration process.

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Advice on Talend, Azure Data Factory

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

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?

269k views269k
Comments
karunakaran
karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k
Comments

Detailed Comparison

Talend
Talend
Azure Data Factory
Azure Data Factory

It is an open source software integration platform helps you in effortlessly turning data into business insights. It uses native code generation that lets you run your data pipelines seamlessly across all cloud providers and get optimized performance on all platforms.

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.

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Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars
-
GitHub Stars
516
GitHub Forks
-
GitHub Forks
610
Stacks
297
Stacks
253
Followers
249
Followers
484
Votes
0
Votes
0
Integrations
No integrations available
Octotree
Octotree
Java
Java
.NET
.NET

What are some alternatives to Talend, Azure Data Factory?

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Kylin

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

Apache Camel

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

Splunk

Splunk

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

Apache Impala

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.

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