Overview

Google Cloud Dataflow

Stacks223

Followers497

Votes19

Talend

Stacks153

Followers249

Votes0

Google Cloud Dataflow vs Talend: What are the differences?

### Introduction
This comparison highlights the key differences between Google Cloud Dataflow and Talend.

1. **Deployment Complexity**: Google Cloud Dataflow, being a managed service, simplifies deployment as it handles infrastructure management and scaling automatically. On the other hand, Talend requires manual deployment and configuration of servers, leading to higher complexity.
2. **Integration Capabilities**: Google Cloud Dataflow is tightly integrated with other Google Cloud services like BigQuery, Pub/Sub, and Data Studio, facilitating seamless data processing. In contrast, Talend offers a more extensive range of connectors, supporting various systems and databases for data integration.
3. **Ease of Use**: Google Cloud Dataflow provides a more intuitive and user-friendly interface for creating data pipelines, making it easier for developers to design and monitor workflows. Talend, while feature-rich, may have a steeper learning curve due to its comprehensive functionality.
4. **Scalability**: Google Cloud Dataflow offers automatic scaling of resources based on workload demand, ensuring efficient use of resources and cost optimization. Talend's scalability relies on manual adjustments and capacity planning, which may lead to underutilization or over-provisioning of resources.
5. **Pricing Model**: Google Cloud Dataflow follows a pay-as-you-go pricing model, where users are charged based on actual usage, offering cost-effectiveness and flexibility. Talend typically involves upfront licensing fees and may require additional costs for support, maintenance, and upgrades, potentially leading to higher overall expenses.
6. **Real-time Processing**: Google Cloud Dataflow supports real-time stream processing with low latency, ideal for applications requiring immediate data insights. Talend, while capable of real-time integration, may not match the speed and responsiveness of Google Cloud Dataflow for real-time processing tasks.

In Summary, Google Cloud Dataflow excels in deployment simplicity, integration with Google Cloud services, ease of use, scalability, flexible pricing, and real-time processing capabilities compared to Talend.

Advice on Google Cloud Dataflow, 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

Detailed Comparison

Google Cloud Dataflow	Talend
Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.	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.
Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;	-
Statistics
Stacks 223	Stacks 153
Followers 497	Followers 249
Votes 19	Votes 0
Pros & Cons
Pros 7 Unified batch and stream processing 5 Autoscaling 4 Fully managed 3 Throughput Transparency	No community feedback yet

What are some alternatives to Google Cloud Dataflow, Talend?

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.

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.