Apache NiFi vs Talend

Overview

Talend

Stacks297

Followers249

Votes0

Apache NiFi

Stacks393

Followers692

Votes65

Apache NiFi vs Talend: What are the differences?

Data Integration Capability: Apache NiFi is primarily designed for data integration and transformation tasks and can handle large amounts of data flow. It offers a visual interface for designing and managing data flows, supporting a wide range of data sources, and providing extensive data processing capabilities. On the other hand, Talend is a comprehensive data integration platform that not only supports data flow management but also offers features like data mapping, data quality, and data governance.
Ease of Use: Apache NiFi has a user-friendly interface that allows users to design, test, and deploy data flows with ease. It provides a drag-and-drop feature for creating data pipelines and offers real-time feedback on the status of the data flow. Talend, on the other hand, provides a more developer-centric approach and requires some level of coding knowledge to create data integration workflows.
Scalability: Apache NiFi is highly scalable and can handle large data volumes and complex data transformations. It offers distributed data processing capabilities, allowing users to scale horizontally by adding more instances to their data flow cluster. Talend, although scalable, may require additional configuration and optimization to handle large-scale data integration tasks.
Connectivity: Apache NiFi offers out-of-the-box connectivity to various data sources, including databases, cloud storage, messaging systems, and APIs. It also supports custom connectors that can be developed using its Java-based API. Talend provides a wide range of connectors for different data sources and offers extensive integration capabilities with popular databases, cloud platforms, and enterprise systems.
Flexibility: Apache NiFi allows users to easily modify and customize data flows using its intuitive graphical interface. It provides a wide range of processors and functions that can be combined to perform complex data transformations. Talend, on the other hand, offers a component-based approach where users can select and configure different components to create data integration workflows. It provides a rich set of pre-built components that can be customized to suit specific data integration requirements.
Monitoring and Management: Apache NiFi provides robust monitoring and management capabilities, allowing users to track the performance of their data flows, monitor data flow patterns, and troubleshoot any issues. It offers real-time reporting and alerts for monitoring data flow statuses. Talend also provides monitoring and management features, including job scheduling, error handling, and performance monitoring, but may require additional configuration and setup.

In summary, Apache NiFi is a powerful and user-friendly data integration tool that offers extensive data processing capabilities and scalability. It provides a visual interface for designing data flows and offers out-of-the-box connectivity to various data sources. Talend, on the other hand, is a comprehensive data integration platform that offers additional features like data mapping, data quality, and data governance. It provides a more developer-centric approach and offers a wide range of connectors for different data sources.

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Advice on Talend, Apache NiFi

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.

80.5k views80.5k

Comments

Detailed Comparison

Talend	Apache NiFi
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.	An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
-	Web-based user interface; Highly configurable; Data Provenance; Designed for extension; Secure
Statistics
Stacks 297	Stacks 393
Followers 249	Followers 692
Votes 0	Votes 65
Pros & Cons
No community feedback yet	Pros 17 Visual Data Flows using Directed Acyclic Graphs (DAGs) 8 Free (Open Source) 7 Simple-to-use 5 Reactive with back-pressure 5 Scalable horizontally as well as vertically Cons 2 Memory-intensive 2 HA support is not full fledge 1 Kkk
Integrations
No integrations available	MongoDB Amazon SNS Amazon S3 Linux Amazon SQS Kafka Apache Hive macOS

What are some alternatives to Talend, Apache NiFi?

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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.

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

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.

Related Comparisons

Apache NiFi vs Talend: What are the differences?

Data Integration Capability: Apache NiFi is primarily designed for data integration and transformation tasks and can handle large amounts of data flow. It offers a visual interface for designing and managing data flows, supporting a wide range of data sources, and providing extensive data processing capabilities. On the other hand, Talend is a comprehensive data integration platform that not only supports data flow management but also offers features like data mapping, data quality, and data governance.
Ease of Use: Apache NiFi has a user-friendly interface that allows users to design, test, and deploy data flows with ease. It provides a drag-and-drop feature for creating data pipelines and offers real-time feedback on the status of the data flow. Talend, on the other hand, provides a more developer-centric approach and requires some level of coding knowledge to create data integration workflows.
Scalability: Apache NiFi is highly scalable and can handle large data volumes and complex data transformations. It offers distributed data processing capabilities, allowing users to scale horizontally by adding more instances to their data flow cluster. Talend, although scalable, may require additional configuration and optimization to handle large-scale data integration tasks.
Connectivity: Apache NiFi offers out-of-the-box connectivity to various data sources, including databases, cloud storage, messaging systems, and APIs. It also supports custom connectors that can be developed using its Java-based API. Talend provides a wide range of connectors for different data sources and offers extensive integration capabilities with popular databases, cloud platforms, and enterprise systems.
Flexibility: Apache NiFi allows users to easily modify and customize data flows using its intuitive graphical interface. It provides a wide range of processors and functions that can be combined to perform complex data transformations. Talend, on the other hand, offers a component-based approach where users can select and configure different components to create data integration workflows. It provides a rich set of pre-built components that can be customized to suit specific data integration requirements.
Monitoring and Management: Apache NiFi provides robust monitoring and management capabilities, allowing users to track the performance of their data flows, monitor data flow patterns, and troubleshoot any issues. It offers real-time reporting and alerts for monitoring data flow statuses. Talend also provides monitoring and management features, including job scheduling, error handling, and performance monitoring, but may require additional configuration and setup.

Apache NiFi vs Talend

Overview