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Google Cloud Dataflow

217
487
+ 1
19
Talend

150
247
+ 1
0
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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 and Talend
karunakaran karthikeyan
Needs advice
on
DremioDremio
and
TalendTalend

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.

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Replies (1)
Rod Beecham
Partnering Lead at Zetaris · | 3 upvotes · 64.4K views
Recommends
on
DremioDremio

Hi Karunakaran. I obviously have an interest here, as I work for the company, but the problem you are describing is one that Zetaris can solve. Talend is a good ETL product, and Dremio is a good data virtualization product, but the problem you are describing best fits a tool that can combine the five styles of data integration (bulk/batch data movement, data replication/data synchronization, message-oriented movement of data, data virtualization, and stream data integration). I may be wrong, but Zetaris is, to the best of my knowledge, the only product in the world that can do this. Zetaris is not a dashboarding tool - you would need to combine us with Tableau or Qlik or PowerBI (or whatever) - but Zetaris can consolidate data from any source and any location (structured, unstructured, on-prem or in the cloud) in real time to allow clients a consolidated view of whatever they want whenever they want it. Please take a look at www.zetaris.com for more information. I don't want to do a "hard sell", here, so I'll say no more! Warmest regards, Rod Beecham.

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Pros of Google Cloud Dataflow
Pros of Talend
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency
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    What is Google Cloud Dataflow?

    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.

    What is Talend?

    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.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Google Cloud Dataflow?
    What companies use Talend?
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    What tools integrate with Google Cloud Dataflow?
    What tools integrate with Talend?

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    What are some alternatives to Google Cloud Dataflow and 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.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Akutan
    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
    Apache Beam
    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
    See all alternatives