Apache Spark vs Talend

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Talend

Stacks297

Followers249

Votes0

Apache Spark vs Talend: What are the differences?

Introduction

Apache Spark and Talend are both popular tools used in data processing and analysis. While they have similarities, there are key differences that set them apart and make them suitable for different use cases. In this article, we will explore and highlight the main differences between Apache Spark and Talend.

Architecture: Apache Spark is a fast and general-purpose cluster computing system that provides in-memory processing for large-scale data processing. It uses a distributed computing model, allowing users to process and analyze data across multiple machines, making it well-suited for big data applications. On the other hand, Talend is an open-source data integration tool that provides a unified platform for designing, deploying, and managing various data integration processes. It follows an extract, transform, and load (ETL) architecture, making it more suitable for traditional data integration scenarios.
Programming Languages: Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. This flexibility allows developers to choose the language they are most comfortable with and leverage existing code and libraries. Talend, on the other hand, primarily uses a Java-based programming language, although it also provides support for other languages through its components. This difference in language support can influence developers' preferences and the availability of libraries in their chosen language.
Data Processing Capabilities: Apache Spark is known for its powerful and scalable data processing capabilities, offering a wide range of built-in libraries and APIs for batch processing, streaming, graph processing, and machine learning. It can handle complex data transformations, aggregations, and analytics efficiently. Talend, on the other hand, focuses more on data integration and ETL processes. While it also provides some data processing functionality, it may not provide the same level of scalability and performance as Apache Spark for advanced data processing tasks.
Data Source and Connectivity: Apache Spark supports a wide range of data sources and formats, including Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, Apache Kafka, and many others. It provides connectors and integrations with various databases and storage systems, making it easy to read and write data from different sources. Talend also provides extensive connectivity options, allowing users to work with various databases, cloud services, file formats, and APIs. However, its focus is primarily on data integration rather than the wide range of data sources supported by Apache Spark.
Deployment Options: Apache Spark can be deployed in various ways, including standalone mode, on-premises clusters, and cloud-based environments. It supports integration with popular cluster managers like Apache Mesos and Hadoop YARN, allowing users to leverage existing infrastructure. Talend, on the other hand, provides both on-premises and cloud deployment options, with support for various cloud platforms, such as AWS, Microsoft Azure, and Google Cloud. It also offers a server-client architecture that allows for centralized management of data integration processes.
Community and Ecosystem: Apache Spark has a vibrant and active community, with a large number of contributors and a rich ecosystem of libraries and tools built on top of it. This ensures continuous development, support, and improvement of the platform. Talend also has a strong community and ecosystem, with a wide range of connectors, components, and extensions available. However, the size and maturity of the Apache Spark community and ecosystem make it a popular choice for many data processing and analytics projects.

In Summary, Apache Spark and Talend are both powerful tools for data processing and analysis, but they differ in their architecture, programming language support, data processing capabilities, data source connectivity, deployment options, and community ecosystem. The choice between the two depends on the specific requirements of the project and the expertise of the development team.

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Advice on Apache Spark, Talend

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:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
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

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k

Comments

Detailed Comparison

Apache Spark	Talend
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 297
Followers 3.5K	Followers 249
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	No community feedback yet

What are some alternatives to Apache Spark, Talend?

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.

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs Talend: What are the differences?

Introduction

Architecture: Apache Spark is a fast and general-purpose cluster computing system that provides in-memory processing for large-scale data processing. It uses a distributed computing model, allowing users to process and analyze data across multiple machines, making it well-suited for big data applications. On the other hand, Talend is an open-source data integration tool that provides a unified platform for designing, deploying, and managing various data integration processes. It follows an extract, transform, and load (ETL) architecture, making it more suitable for traditional data integration scenarios.
Programming Languages: Apache Spark supports multiple programming languages, including Java, Scala, Python, and R. This flexibility allows developers to choose the language they are most comfortable with and leverage existing code and libraries. Talend, on the other hand, primarily uses a Java-based programming language, although it also provides support for other languages through its components. This difference in language support can influence developers' preferences and the availability of libraries in their chosen language.
Data Processing Capabilities: Apache Spark is known for its powerful and scalable data processing capabilities, offering a wide range of built-in libraries and APIs for batch processing, streaming, graph processing, and machine learning. It can handle complex data transformations, aggregations, and analytics efficiently. Talend, on the other hand, focuses more on data integration and ETL processes. While it also provides some data processing functionality, it may not provide the same level of scalability and performance as Apache Spark for advanced data processing tasks.
Data Source and Connectivity: Apache Spark supports a wide range of data sources and formats, including Hadoop Distributed File System (HDFS), Apache Cassandra, Apache HBase, Apache Kafka, and many others. It provides connectors and integrations with various databases and storage systems, making it easy to read and write data from different sources. Talend also provides extensive connectivity options, allowing users to work with various databases, cloud services, file formats, and APIs. However, its focus is primarily on data integration rather than the wide range of data sources supported by Apache Spark.
Deployment Options: Apache Spark can be deployed in various ways, including standalone mode, on-premises clusters, and cloud-based environments. It supports integration with popular cluster managers like Apache Mesos and Hadoop YARN, allowing users to leverage existing infrastructure. Talend, on the other hand, provides both on-premises and cloud deployment options, with support for various cloud platforms, such as AWS, Microsoft Azure, and Google Cloud. It also offers a server-client architecture that allows for centralized management of data integration processes.
Community and Ecosystem: Apache Spark has a vibrant and active community, with a large number of contributors and a rich ecosystem of libraries and tools built on top of it. This ensures continuous development, support, and improvement of the platform. Talend also has a strong community and ecosystem, with a wide range of connectors, components, and extensions available. However, the size and maturity of the Apache Spark community and ecosystem make it a popular choice for many data processing and analytics projects.

Apache Spark vs Talend

Overview