Scale and Optimize Data Pipelines – Six Best Practices to Implement

In today’s highly competitive business environment, data is the single most important factor that can help a firm improve its standing in the market. Data Pipelines may be found almost anywhere, and it is essential to gather it and store it in the appropriate format in order to conduct additional analysis and derive insights that can be put into action in order to make data-driven business choices. Data Engineers are the ones who enter the picture at this point.

Scale and Optimize Data Pipelines - Six Best Practices to Implement

According to the news portal for big data known as Datanami, Data Engineers have emerged as significant resources because of their ability to capitalize on the value of data for the sake of achieving business goals. They fulfill a crucially important strategic function for the company despite the challenging environment in which they operate. Data Engineers are also responsible for the administration of data, which includes ensuring that data is sent to end users so that they can develop reports, insights, dashboards, and feeds to other systems that are further downstream.

To establish data pipelines and move massive amounts of data, an ETL tool, which stands for “Extract, Transform, and Load,” is typically utilized. However, in the modern world, when large amounts of data are readily available, performing real-time data analysis is absolutely important in order to make speedy evaluations and take appropriate actions. Instead of manually building ETL code and cleaning the data, businesses are turning to the knowledge of data engineers to assist data strategy and pipeline optimization.

This blog post will go over some useful hints and guidelines to bear in mind when optimizing your ETL tools, along with several use cases to help better comprehend them.

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The Best Ways to Improve Your Data Pipeline

While transporting enormous amounts of data presents a number of issues, the primary objective in optimizing data is to cut down on data loss and ETL run downtime as much as possible. Following are some of the most efficient ways to optimize the data pipeline:

  1. Instead of executing all of the data in a sequential fashion, you should consider using concurrent or simultaneous data flow. This can save a significant amount of time. If the data flow is independent of one another, it is possible to accomplish this. For instance, it is necessary to import fifteen structured data tables from one source into another. However, because the data tables do not depend on one another, we can execute three batches of tables in parallel rather than going through all of the tables one at a time in a specific order. As a consequence of this, each batch is able to simultaneously process five tables. As a result, the amount of time required to complete a pipeline run is one-third that of a serial run.
  2. Implement Data Quality Checks Because the quality of the data can be compromised at any level, data engineers are obligated to make an effort to guarantee that the quality of the data is high. An example of such a check is the utilization of a schema-based test, in which each data table may be checked using predetermined checks. These tests may include the data type of the column as well as the presence of null or blank data. If the data checks are passed, then it is possible to produce the desired output; otherwise, the input is refused. Additionally, indexing can be added to the table in order to prevent the creation of duplicate records.
  3. Make Use of Generic Pipelines: In order to carry out their analysis, several groups, both inside and outside of your team, frequently require the same fundamental data. If a same pipeline or piece of code is used multiple times, the same section of code can be reused. In the event that a new pipeline needs to be constructed, we are able to leverage the pre-existing code in the appropriate places. Because of this, we are able to reuse pipeline assets when developing new pipelines, which eliminates the need to design these pipelines from the ground up.

Introduce Email Notification: When manually monitoring job execution, we have to look at the log file in great detail, which is a time-consuming process. Sending an email notification that provides information relating to the running status of a job and sends an email in case of failure is the solution to this problem. This may be accomplished by sending an email notice. This leads in a shorter response time and the task is restarted from the point where it failed in a shorter amount of time with a greater degree of accuracy.

4. In order to make a pipeline general, parameterization of the parameters should be practiced rather than hardcoding the values. The usage of parameters makes it possible to easily run the job by altering only the values, which allows for greater flexibility. For instance, the specifics of database connections can differ from one team to the next, and connection values are subject to change. It will be helpful to provide these values on to the program in the form of parameters in situations like these. Using this pipeline is going to be a breeze for the group now that they have changed the connection parameters and started the job.

5. Implement Documentation: Let’s imagine a new person has joined the team, and he needs to start working on the existing project as soon as possible. In this scenario, the documentation should be implemented. He gives needs in an effort to gain an understanding of the work that has been completed to this point, but the team has not yet documented the flow of data. Because of this, it will be difficult for the new member to learn the current workflow and process, which may ultimately result in delivery delays. Therefore, having a flow that has been thoroughly documented might be useful as a reference to comprehending the entire workflow. If you want to comprehend something better, using a flowchart is the best option. Take a look at the ETL flow that was provided below. It illustrates the three fundamental stages of the data flow.

  1. Data extraction from the source and transfer to the staging area,
  2. In the staging area, the transformation of the data,
  3. Putting newly formatted information into a data warehouse

6. Streaming should be used rather than batching because businesses typically build up their data stores during the day. Because of this, batch ingestions performed on a regular basis may overlook some events. This can have major repercussions, such as the inability to spot anomalies or fraudulent activity. Establish continuous streaming ingestion as an alternative to cut down on pipeline delay and provide the company with the ability to access recent data.

There is more ground to cover

The aforementioned tips are generic but can be tailored to meet any data optimization concerns. In addition, there are a lot of other techniques to optimize your pipeline, such as improving the transformations and data filtering that take place before passing through a pipeline in order to reduce demand. When it comes to constructing data pipelines, rigorous data processing is crucial; nevertheless, data engineers need to ensure that the operations team can utilize and govern the pipeline. This is in addition to ensuring that the pipeline can be used. These best practices for data engineering will help guarantee that your data pipelines are scalable, valid, reusable, and production-ready so that data consumers like data scientists can use them for analysis.

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