by Navdeep Singh Gill | Nov 15, 2021 12:00:00 AM
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In today's world, organizations are moving towards data-driven decisions making. They are using data to create applications that can help them to make decisions. Data helps them to improve their productivity and revenue by working on the business challenges. Using data, organizations get to know the market trends, customer experience and can take actions accordingly.
But, It is not always possible, data that they need, placed at only one location. The data they need may be placed at several locations; therefore, they need to approach several sources to collect data. That is a time-consuming process as well as a complicated task as the data volume increases.
To solve these issues, they require Data Pipeline to have data available that can be used to get insights and make decisions. Data Pipeline helps to extract data from various sources and transform them to use it. Data pipelines can be used to transfer data from one place to another, ETL (Extract-Transform-Load), ELT (Extract-Load-Transform), data enrichment, and real-time data analysis.
Data Pipeline is a series of steps that collect raw data from various sources, transform, combine, validate, and transfer them to a destination. It eliminates the manual task and allows the data to move smoothly. Thus It also eliminates manual errors. It divides the data into small chunks and processes it parallelly, thus reducing the computing power.
According to the purpose, there are different types of data pipeline solutions. Such as:
Source: The data pipeline collects the data from the data source; it can be a source of generation or any data storage system.
Destination: This is the point where data to be transferred. The destination point depends on the application for which data is extracted. It can be an analytics tool or a storage place.
The Data Pipeline is important when
When the data is stored at different locations, and their combined analysis is required. For instance, there is a system for e-commerce that requires the user's personal and product purchase data to target the customers. Therefore, it needs a data pipeline to collect data from CRM (where the user's data is saved) and website data regarding their purchase, order, and visit data.
It may be possible that there is a large volume of data, and analysis applications are also very large and complex. It may be possible that using the same system for analysis where data generated or present slows down the analysis process. As a result, to reduce the effect on the performance of the service data pipeline is required.
When analysis and production teams are different, user organizations do not want the analysis team to access the production system. In a case, if it is not necessary to share the whole data with the data analyst.
To reduce the interruptions.
To automate the process so that every time does not have to collect data manually whenever it is required.
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The challenges of Data Pipelines are listed below:
Depending on data consumption, enterprises may need to migrate their data from one place to another. Many companies run batch jobs at night time to take advantage of non-peak hours compute resources. Therefore, you see yesterday’s data as of today’s data, making real-time decisions impossible.
Apache Kafka tends to be preferred for large-scale data ingestion. It partitions data so that producers, brokers, and consumers can scale automatically as workload and throughput expand.
Today’s data volume and velocity can render your approach to import data in isolated, all-or-nothing atomic batches. The best solution is a managed system that provides automatic scaling of data storage and worker nodes. It will provide ease at managing the unforeseeable workloads of different traffic patterns.
For example, in the case of Kinesis Data Stream, a CloudWatch alarm watches Kinesis Data Stream shard metrics, and a custom threshold of the alarm is set up. The alarm threshold is reached as the number of requests has grown and the alarm is fired. This firing sends a notification to an Application Auto Scaling policy that will scale up the number of kinesis data stream shards.
Another example is the auto-scaling of data processing. In the case of Lightweight ETL like Lambda Pipeline, AWS Lambda will create an instance of the function the first time you invoke a lambda function and run its handler method. Now, if the function is invoked again while the first event is being processed, Lambda initializes another instance, and the lambda function processes two events concurrently. As more events come in, the lambda function creates new instances as needed. As the requests decrease, Lambda releases the scaling capacity for other functions.
In the case of Heavyweight ETL like Spark, the cluster can scale up and down the processing unit (Worker Nodes) depending upon the workload.
The performance of the pipeline can be enhanced by reducing the size of the data you are ingesting, that is, ingesting only necessary raw data and keeping the intermediate data frames compact. Reading only useful data into memory can speed up your application.
It is equally important to optimize the writes of data you perform in the pipeline. Writing less output into the destination source also improves the performance easily. This step often gets less attention. The data written to data warehouses are mostly not compact. It will not only help to reduce the cost of data storage but also the cost of downstream processes to handle unnecessary rows. So, tidying up your pipeline output spreads performance benefits to the whole system and company.
Through data caching, the outputs or downloaded dependencies from one run can be reused in later runs, thereby improving the build time. It will help in reducing the cost to recreate or redownload the same files again. It is helpful in situations where at the start of each run, the same dependencies are downloaded again and again. This is a time-consuming process involving a large number of network calls.
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The best practices of Data Pipelines are mentioned below:
The first step is to recognize which pipeline you need for data handling. There are three types of pipelines:
Serverless Data Pipeline should be preferred while choosing the data pipeline as it helps in cost optimization.
Concurrent data flow can save considerable time instead of sequential data runs. Concurrent data flow is possible if the data doesn’t depend on one another.
For example, the user needs to import 10 structured data tables from one data source to another. The data tables don’t depend on each other, so we can run two batches of tables parallelly instead of running all the tables in a sequence. As a result, each batch will run five tables synchronously and help to reduce the runtime of the pipeline to half of the serial run.
Data Quality needs to be maintained at any level so that the trustworthiness and value of data cannot be jeopardized. Teams can introduce schema-based tests, which test each data table with predefined checks, including the presence of null/blank data and column data type. The desired output can be produced if the data checks match, else, the data is rejected. Users can also perform indexing in the table to avoid duplicate records.
For example, in a data pipeline, data received from the client is in protobuf format. We will by-parse protobuf to extract the data, and which will automatically apply a schema-based test on the data, where the data failing will be put into a different location to be handled explicitly, and processed data will be taken to a different stream.
Another example is of using AWS Deequ for data quality checks. We can use Deequ to compute data quality metrics like completeness, maximum, or correlation and be defined about changes in data distribution. AWS Deequ can help us in building scheduled data quality metrics and allow us to define special handling, like alarms based on quality.
Multiple groups often need to use the same core data to perform analysis. The same piece of code can be reused if a particular pipeline/code is repeated or a new pipeline needs to be built.
This will help to save energy and time since we can reuse pipeline assets to create new pipelines rather than developing a new one from scratch every time. You can parameterize the values used in the pipelines instead of hardcoding them. Different groups can use the same code by simply changing the parameters according to their specifications.
For example, the developer can build a boilerplate code so that it can be used by different teams with or without little alterations. Let’s consider we have a code that requires database connection details. The team can create a generic utility and pass the values in the form of parameters. Now, the different teams can use this utility, change the database connection details according to needs and run the job.
Monitoring the job manually can be tedious since you need to look closely at the log file. The user can automate this process by clicking on the option to get email notifications related to the running status of a job and get notified in case of job failure. This improves the response time and restarts the work in a short duration of time with greater accuracy.
For example, while monitoring the Kinesis stream, the developer can view the write and read throughput. If the Write and Read Throughput exceeds the threshold, the developer can scale out the shards and if the pipeline resource usage is less than estimated, the developer can scale down the resources and improve cost optimization. The developer can get alerts through email or slack so that they can act spontaneously in case of need.
Well-documented data flow can help new team members to understand the project details. Using flowcharts is preferred for better understanding. Documenting the code is also considered as the best practice since it will help new team members to get an easy understanding and walk-through of the code architecture and working.
For example, consider the ETL flow given below.
Data is extracted from the source to the staging area,
Data is transformed in the staging area,
Transformed data is loaded to a DataWarehouse
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Most people use the Data pipeline and ETL interchangeably. But they are different ones. ETL is a specific type of pipeline; it is a subset of the whole pipeline process. ETL used for Extract(pull), Transform(modify) and then Load(insert) data. Historically it is used as a batch workload, which means it is configured to run batches on a particular day or time only. But now, a new ETL tool is emerging for real-time streaming events also.
A data pipeline is a broader term used to transfer data from one system to another. Every time it doesn't need to transform the data.
Data collection, transformation, and movement are time-consuming and complex tasks, but a Data pipeline makes it easy for everyone who uses data to make strategic and operational decisions. Organizations can use a data pipeline that uses data stored at different locations and analyzes a broad view of the data. It makes the application more accurate, efficient, and acceptable using diverse data.
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