AWS Glue vs Apache Spark vs Talend

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AWS Glue

462
818
+ 1
9
Apache Spark

3K
3.5K
+ 1
140
Talend

153
249
+ 1
0
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Pros of AWS Glue
Pros of Apache Spark
Pros of Talend
  • 9
    Managed Hive Metastore
  • 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
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
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    Cons of AWS Glue
    Cons of Apache Spark
    Cons of Talend
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      • 4
        Speed
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        What is AWS Glue?

        A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

        What is 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.

        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.

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        What companies use AWS Glue?
        What companies use Apache Spark?
        What companies use Talend?

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        What tools integrate with AWS Glue?
        What tools integrate with Apache Spark?
        What tools integrate with Talend?

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        What are some alternatives to AWS Glue, Apache Spark, and Talend?
        AWS Data Pipeline
        AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
        Airflow
        Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
        Alooma
        Get the power of big data in minutes with Alooma and Amazon Redshift. Simply build your pipelines and map your events using Alooma’s friendly mapping interface. Query, analyze, visualize, and predict now.
        Databricks
        Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
        MySQL
        The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
        See all alternatives