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Modern data analytics is characterized as data pipelines. Here multitude of queries are unified by their outputs to implement crucial business activities.
There have been quite a few tools that have emerged in the last few years to curate and manage these data pipelines. This includes Airflow, Azure Data Factory, Dagster, AWS Data Pipeline, and Google Dataflow.
With the help of these tools, users can ascertain data pipelines and efficiently run them in the cloud.
Since these workloads have become interconnected, it is imperative to optimize their performance and costs holistically.
This is where Microsoft Data and AI services can come in handy. You can use the services of Microsoft Data and AI Consulting from an efficient Microsoft Data and AI service provider.
We even suggest you see whether the Microsoft Data and AI service provider you wish to go for is a Microsoft Data and AI partner or not. If it is not, you should look for some other option.
In the forthcoming section, we will look at the ways to optimize data pipelines with Microsoft’s AI frameworks.
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Optimizing Data Pipelines with Microsoft’s AI Framework
Data pipelines use two stages for optimization with Microsoft’s AI framework: derive and apply.
In the derive stage, data pipelines accumulate, from every data pipeline, customer job requirements. This includes operator push-up and statistics in a bottom-up manner.
In the apply stage, data pipelines apply its optimizations and create recommendations. These recommendations are surfaced to users.
Scalable optimization are the phases that are scaled by mapping them to a massively parallel SCOPE engine.
Derive
The derive stage takes as input the producer-consumer graph from the data pipeline discovery phase. The first step is, in a bottom-up manner, it ascertains the consumer job requirements that producer jobs must satisfy.
A prime example of that is the output of producer jobs must be sorted on P.x, statistics are required on P.y and P.yz, the output of producer job must project away columns P.a and P.b, the output of producer job must satisfy filler predicates in consumer jobs, etc.
The requisites of distinct consumer jobs can be conflicting. For example, the consumer job may require the output of a producer job to be sorted on P.x, while another requires the output to be sorted on P.y.
Data pipelines select the requirements that optimize the overall pipeline, which we will explain in the next stage, i.e., Apply.
Apply
Data pipelines systematically syndicate the requirements accumulated in the Derive stage from each producer job along with the following dimensions: partitioning columns, statistics collection columns, sorting columns, filter predicates, and projection push-up columns.
Along with each dimension, the data pipeline selects consumer requirements that optimize the complete pipeline to be pushed to the producer jobs.
A prime example is, for projection OPERATOR PUSH-UP, it opts for an intersecting set of columns that satisfies all consumer jobs when pushed up to producer jobs.
For PHYSICAL DESIGN, data pipelines also recommend dividing and sorting the output of producer jobs. This will eliminate the need for multiple consumer jobs to re-partition or re-sort data while satisfying storage and compute constraints.
The Problem of Deciding the Best Data Transformation Pipeline Tool
Data transformation is a complicated field because it features different requirements and needs contingent upon data volume, variety, velocity, and veracity. This complexity results in diverse tools. Each caters to a distinct need.
There are different reasons as to why there will be no solitary best data transformation pipeline tool.
Distinct Use Cases
Distinct tools are optimized for distinct use cases. Certain tools are excellent for batch processing. This includes Azure Data Factory. While some are built for real-time streaming, like Azure Stream Analytics, others might be more apt for ML workflows, like Azure Machine Learning.
Depending upon your use case, you can opt for a solitary tool or a mix of tools as your requirement.
Different Data Sources
In the digital era we are living in, data comes from different sources like IoT devices, APIs, traditional databases, cloud services, etc. Every source might require a distinct tool to extract and manage data efficiently.
Processing Requirements
The computational requirements may vastly differ on the basis of the nature of the transformation. A lightweight, serverless function might be able to handle simple transformations. On the other hand, complex, data-intensive tasks might need a strong big data solution like Azure Databricks.
Cost
Every tool has a separate pricing structure. One tool might be preferred over the other, depending on the budget and cost-effectiveness.
Final Words
This is all we have for you when it comes to optimizing data pipelines with Microsoft’s AI frameworks. If you wish to get the most out of data pipelines, we suggest you affiliate with the right Microsoft Data and AI partner and avail of their Microsoft Data and AI Services from an experienced Microsoft Data and AI Consultant.
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