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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 · 50.8K views
Recommends
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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AirflowAirflowLuigiLuigi
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Apache SparkApache Spark

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 210.8K views
Recommends
CassandraCassandra

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Talend
  • 50
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Custom operators
  • 3
    Apache project
  • 3
    Good api
  • 2
    Dashboard
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    Cons of Airflow
    Cons of Talend
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
    • 1
      Observability is not great when the DAGs exceed 250
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      What is 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.

      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 are some alternatives to Airflow and Talend?
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