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Apache Beam

162
312
+ 1
14
Luigi

67
171
+ 1
8
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Apache Beam vs Luigi: What are the differences?

Apache Beam: A unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments; Luigi: *ETL and data flow management library *. It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Apache Beam and Luigi belong to "Workflow Manager" category of the tech stack.

Luigi is an open source tool with 12K GitHub stars and 1.98K GitHub forks. Here's a link to Luigi's open source repository on GitHub.

According to the StackShare community, Apache Beam has a broader approval, being mentioned in 9 company stacks & 4 developers stacks; compared to Luigi, which is listed in 6 company stacks and 3 developer stacks.

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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 · 164.6K 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 Apache Beam
Pros of Luigi
  • 5
    Open-source
  • 5
    Cross-platform
  • 2
    Portable
  • 2
    Unified batch and stream processing
  • 5
    Hadoop Support
  • 2
    Python
  • 1
    Open soure

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What is Apache Beam?

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

What is Luigi?

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

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What companies use Apache Beam?
What companies use Luigi?
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What tools integrate with Apache Beam?
What tools integrate with Luigi?
What are some alternatives to Apache Beam and Luigi?
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 Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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.
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.
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