MLflow pipelines on databricks - optimization

I need to run four pyspark scripts over a databricks cluster back to back as steps in mlflow as a pipeline. Can someone please help me with any online content for this. This is an optimization algo model where there is no .pkl file involved to be read and make prediction . It can be considered as four pyspark scripts to be triggered back to back as a pipeline over MLflow in a batch mode.
I know it works in Azure ML studio where we make a pipeline and define these steps where each step is tagged to a .py script. I want to replicate a similar scenario here.

Related

Customize Airflow / TFX Pipeline to upload Files in S3

I am currently trying to customize a tfx pipeline, which is being orchestrated by Airflow.
Basically I want to upload all generated files by the DAG to a S3 bucket storage after the run of the pipeline.
The issue is that every tfx pipeline component gets parsed into a individual DAG, because of this I can not access these via abc.downstream("xy")
By my understanding, see this documentation for how the pipeline gets build dag-runner.
My code is based on the tfx-taxi-example.
Maybe I have not fully grasped the concepts of the TFX pipeline yet, so please make me aware if I am trying to solve this in a overly complex way.
I would greatly appreciate an alternative approach to what I am trying to accomplish ;)

Which is a more efficient orchestrating mechanism, chaining Databricks notebooks together or using Apache Airflow?

The data size for the data is in the Terabytes.
I have multiple Databricks notebooks for incremental data load into Google BigQuery for each dimension table.
Now, I have to perform this data load every two hours i.e. run these notebooks.
What is a better approach among the following:
Create a master Databricks notebook and use dbutils to chain/parallelize the execution of the aforementioned Databricks notebooks.
Use Google Composer (Apache Airflow's Databricks Operator) to create a master DAG to orchestrate these notebooks remotely.
I want to know which is better approach when I have use cases for both parallel execution and sequential execution of said notebooks.
I'd be extremely grateful if I could get a suggestion or opinion on this topic, thank you.
why can't you try with databricks jobs . So that you can use job for way of running a notebook either immediately or on a scheduled basis.

How to process org.apache.spark.sql.Dataset with apache beam?

I want to process the data from org.apache.spark.sql.Dataset Object that I am retrieving from spark.sql(" query ") with Apache beam.
But I am not able to apply PTransform to this Dataset directly. Right now I am using a solution in which I dump the Dataset to txt file and then process it with beam.
Is there any way in which I can consume Dataset / JavaRDD directly into the beam (Ptransform)?
I am using apache beam 2.9.0.
To my knowledge you cannot consume Dataset into Beam now. Dataset is Spark's API that it's coupled with Spark internal implementations. So you cannot start Spark job, run until a step, and then let Beam takes it over.
Your approach is the right approach to connect two different system's pipeline. Even for pipelines of the same type system, data sharing usually is the only way to connect different pipelines.
There is one possibility to make Spark on Beam work, which is automatically generate Beam code from Spark code. It does not exist in open source world, but you can read some information from LinkedIn's practice: https://engineering.linkedin.com/blog/2019/01/bridging-offline-and-nearline-computations-with-apache-calcite

Sequential Scripts conditioned by S3 file existence

I have three python scripts. These are supposed be executed sequentially, but in different environments.
script1: Generate training and test dataset using an AWS EMR cluster and save it on S3.
script2: Train a Machine Learning model using the training data and save the trained model on S3. (Executed on an AWS GPU instance)
script3: Run evaluation based on the test data and trained model and save the result on S3. (Executed on an AWS GPU instance)
I would like to run all these scripts automatically, without executing them one by one. My questions are:
Are there good practices for handling S3 file existence conditions? (false tolerence etc)
How can I trigger launching GPU instances and EMR clusters?
Are there good ways or tools to handle this kind of process?
Take a look at https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html
You can configure your notification when an event occur on a bucket, for example when an object is created.
You can attach this notification directly to an AWS lambda function that, if will be set with the right role can create EMR cluster and all other resources accessible by AWS SDK.

Apache Airflow or Apache Beam for data processing and job scheduling

I'm trying to give useful information but I am far from being a data engineer.
I am currently using the python library pandas to execute a long series of transformation to my data which has a lot of inputs (currently CSV and excel files). The outputs are several excel files. I would like to be able to execute scheduled monitored batch jobs with parallel computation (I mean not as sequential as what I'm doing with pandas), once a month.
I don't really know Beam or Airflow, I quickly read through the docs and it seems that both can achieve that. Which one should I use ?
The other answers are quite technical and hard to understand. I was in your position before so I'll explain in simple terms.
Airflow can do anything. It has BashOperator and PythonOperator which means it can run any bash script or any Python script.
It is a way to organize (setup complicated data pipeline DAGs), schedule, monitor, trigger re-runs of data pipelines, in a easy-to-view and use UI.
Also, it is easy to setup and everything is in familiar Python code.
Doing pipelines in an organized manner (i.e using Airflow) means you don't waste time debugging a mess of data processing (cron) scripts all over the place.
Nowadays (roughly year 2020 onwards), we call it an orchestration tool.
Apache Beam is a wrapper for the many data processing frameworks (Spark, Flink etc.) out there.
The intent is so you just learn Beam and can run on multiple backends (Beam runners).
If you are familiar with Keras and TensorFlow/Theano/Torch, the relationship between Keras and its backends is similar to the relationship between Beam and its data processing backends.
Google Cloud Platform's Cloud Dataflow is one backend for running Beam on.
They call it the Dataflow runner.
GCP's offering, Cloud Composer, is a managed Airflow implementation as a service, running in a Kubernetes cluster in Google Kubernetes Engine (GKE).
So you can either:
manual Airflow implementation, doing data processing on the instance itself (if your data is small (or your instance is powerful enough), you can process data on the machine running Airflow. This is why many are confused if Airflow can process data or not)
manual Airflow implementation calling Beam jobs
Cloud Composer (managed Airflow as a service) calling jobs in Cloud Dataflow
Cloud Composer running data processing containers in Composer's Kubernetes cluster environment itself, using Airflow's KubernetesPodOperator (KPO)
Cloud Composer running data processing containers in Composer's Kubernetes cluster environment with Airflow's KPO, but this time in a better isolated fashion by creating a new node-pool and specifying that the KPO pods are to be run in the new node-pool
My personal experience:
Airflow is lightweight and not difficult to learn (easy to implement), you should use it for your data pipelines whenever possible.
Also, since many companies are looking for experience using Airflow, if you're looking to be a data engineer you should probably learn it
Also, managed Airflow (I've only used GCP's Composer so far) is much more convenient than running Airflow yourself, and managing the airflow webserver and scheduler processes.
Apache Airflow and Apache Beam look quite similar on the surface. Both of them allow you to organise a set of steps that process your data and both ensure the steps run in the right order and have their dependencies satisfied. Both allow you to visualise the steps and dependencies as a directed acyclic graph (DAG) in a GUI.
But when you dig a bit deeper there are big differences in what they do and the programming models they support.
Airflow is a task management system. The nodes of the DAG are tasks and Airflow makes sure to run them in the proper order, making sure one task only starts once its dependency tasks have finished. Dependent tasks don't run at the same time but only one after another. Independent tasks can run concurrently.
Beam is a dataflow engine. The nodes of the DAG form a (possibly branching) pipeline. All the nodes in the DAG are active at the same time, and they pass data elements from one to the next, each doing some processing on it.
The two have some overlapping use cases but there are a lot of things only one of the two can do well.
Airflow manages tasks, which depend on one another. While this dependency can consist of one task passing data to the next one, that is not a requirement. In fact Airflow doesn't even care what the tasks do, it just needs to start them and see if they finished or failed. If tasks need to pass data to one another you need to co-ordinate that yourself, telling each task where to read and write its data, e.g. a local file path or a web service somewhere. Tasks can consist of Python code but they can also be any external program or a web service call.
In Beam, your step definitions are tightly integrated with the engine. You define the steps in a supported programming language and they run inside a Beam process. Handling the computation in an external process would be difficult if possible at all*, and is certainly not the way Beam is supposed to be used. Your steps only need to worry about the computation they're performing, not about storing or transferring the data. Transferring the data between different steps is handled entirely by the framework.
In Airflow, if your tasks process data, a single task invocation typically does some transformation on the entire dataset. In Beam, the data processing is part of the core interfaces so it can't really do anything else. An invocation of a Beam step typically handles a single or a few data elements and not the full dataset. Because of this Beam also supports unbounded length datasets, which is not something Airflow can natively cope with.
Another difference is that Airflow is a framework by itself, but Beam is actually an abstraction layer. Beam pipelines can run on Apache Spark, Apache Flink, Google Cloud Dataflow and others. All of these support a more or less similar programming model. Google has also cloudified Airflow into a service as Google Cloud Compose by the way.
*Apache Spark's support for Python is actually implemented by running a full Python interpreter in a subprocess, but this is implemented at the framework level.
Apache Airflow is not a data processing engine.
Airflow is a platform to programmatically author, schedule, and
monitor workflows.
Cloud Dataflow is a fully-managed service on Google Cloud that can be used for data processing. You can write your Dataflow code and then use Airflow to schedule and monitor Dataflow job. Airflow also allows you to retry your job if it fails (number of retries is configurable). You can also configure in Airflow if you want to send alerts on Slack or email, if your Dataflow pipeline fails.
I am doing the same as you with airflow, and I've got very good results. I am not very sure about the following: Beam is machine learning focused and airflow is for anything you want.
Finally you can create a hive with kubernetes +airflow.