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  5. Azure Synapse vs Talend

Azure Synapse vs Talend

OverviewDecisionsComparisonAlternatives

Overview

Talend
Talend
Stacks297
Followers249
Votes0
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs Talend: What are the differences?

Introduction:

Azure Synapse and Talend are two popular data integration and analytics platforms used by organizations for managing and processing data. While both offer similar functionalities, there are key differences that set them apart. In this article, we will explore these differences in detail and provide a clear comparison between Azure Synapse and Talend.

  1. Data integration capabilities: Azure Synapse is a fully-managed data integration and analytics service provided by Microsoft. It offers seamless integration with various data sources, including SQL data warehouses, big data, and streaming data. Azure Synapse provides powerful data movement and transformation capabilities, allowing users to ETL (Extract, Transform, Load) data from multiple sources into a unified data repository. On the other hand, Talend is an open-source data integration platform that supports a wide range of data integration scenarios. With Talend, users can design and execute complex data integration workflows, including data cleansing, enrichment, and consolidation.

  2. Scalability and performance: Azure Synapse is designed to handle large-scale data processing and analytics workloads. It offers massive scalability, allowing users to scale up or down resources based on demand. Azure Synapse leverages distributed computing technologies to achieve high-performance processing of data. In contrast, Talend's scalability and performance depend on the underlying infrastructure on which it is deployed. While it can handle moderate data volumes efficiently, scaling Talend to handle big data workloads may require additional infrastructure provisioning and configuration.

  3. Native integration with Azure services: Azure Synapse is tightly integrated with various Azure services, such as Azure Data Lake Storage, Azure Active Directory, and Azure Machine Learning. This allows seamless data access and integration with other Azure services, enabling users to leverage the full power of the Azure ecosystem. On the other hand, Talend can integrate with Azure services using connectors and APIs, but it may require additional configuration and setup to achieve seamless integration.

  4. Pricing model: Azure Synapse follows a pay-as-you-go pricing model based on resource usage. Users are billed for the amount of data processed and the resources consumed during data integration and analytics operations. Azure Synapse offers different pricing tiers to accommodate different usage and performance requirements. Talend, on the other hand, offers a subscription-based pricing model, where users pay a fixed fee based on the selected Talend edition. The pricing for Talend includes support and maintenance services, which may be a preferred option for organizations looking for predictable costs.

  5. Ease of use and user interface: Azure Synapse provides a user-friendly interface that simplifies the process of creating and managing data integration workflows. It offers a visual drag-and-drop interface for designing data pipelines and supports SQL-like query languages for data processing. Talend also provides a user-friendly interface with a visual design environment for creating data integration jobs. It supports a wide range of data sources and provides connectors for various systems and databases. However, Talend may require more technical expertise and familiarity with its interface and components compared to Azure Synapse.

  6. Community and support: Azure Synapse benefits from the extensive support and community of Microsoft, which provides regular updates, documentation, and troubleshooting resources. Microsoft offers comprehensive support services for Azure Synapse, including technical support and service level agreements. Talend, being an open-source platform, has its own community and support resources. While Talend's community provides active forums, documentation, and resources, the level of support may depend on the specific edition and support subscription chosen.

In summary, Azure Synapse and Talend are both powerful data integration and analytics platforms, but they differ in terms of their data integration capabilities, scalability and performance, native integration with Azure services, pricing model, ease of use and user interface, and community and support. The choice between Azure Synapse and Talend depends on specific requirements, budget, and the level of integration and support needed within the Azure ecosystem.

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Advice on Talend, Azure Synapse

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.

80.5k views80.5k
Comments

Detailed Comparison

Talend
Talend
Azure Synapse
Azure Synapse

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 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.

-
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
297
Stacks
104
Followers
249
Followers
230
Votes
0
Votes
10
Pros & Cons
No community feedback yet
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI

What are some alternatives to Talend, Azure Synapse?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

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