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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:
Let's discuss the components of the data pipeline to understand its work that how does it make and move data:
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.
Let's look at the significant components of a typical data pipeline to see how they prepare massive datasets for analysis. These are the following:
A pipeline extracts data from these locations. Relational database management systems (RDBMS), customer relationship management systems (CRMs), enterprise resource planning systems (ERPs), social media management tools, and even IoT device sensors are examples.
This is the data pipeline's endpoint, where all of its extracted data is dumped. A data pipeline's final destination is frequently a data lake or a data warehouse, which is kept for analysis. That isn't always the case, though. Data can be supplied directly into data visualization tools for analysis.
As data moves from source to destination, it changes. Dataflow is the term for the movement of data. ETL, or extract, transform, and load, is one of the most prevalent dataflow methodologies.
Steps involved in extracting data from various sources, altering it, and transporting it to a specific location. A data pipeline's processing component determines how data flow should be implemented. What extraction procedure should be utilized for ingesting data, for example. Batch processing and stream processing are two typical ways of extracting data from sources.
Workflow is concerned with how jobs in a data pipeline are executed and their interdependence. When a data pipeline runs, it is determined by its dependencies and sequencing. In most data pipelines, upstream activities must be completed successfully before downstream jobs may begin.
To ensure data correctness and prevent data loss, a data pipeline must be monitored on a regular basis. A pipeline's speed and efficiency are also checked, especially as the bulk of the data grows higher.
The Data Pipeline is important when the data is stored at different locations, 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, reducing 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 this case, 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:
Click to explore: Azure Data Factory vs. Apache Airflow
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, 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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A data pipeline is just a sequence of steps transporting raw data from one location to another. A transactional database could be a source in the context of business intelligence. The data is analyzed for business insights at the destination. Transformation logic is applied to data during this journey from the source to the destination to prepare it for analysis. There are numerous advantages to this method; here are our top six.
Individual pipes are seen as examples of patterns in a more extensive architecture that may be reused and repurposed for new data flows when data processing is viewed as a network of pipelines.
Having a familiar concept and tools for how data should flow through analytics systems makes it easier to plan to ingest new data sources and minimizes the time and expense of integrating them.
Thinking of your data flows as pipelines that need to be monitored and meaningful to end users increases data quality and minimizes the chances of pipeline breakdowns going undiscovered.
Security is baked in from the start by having repeatable patterns and a consistent understanding of tools and architectures. Reasonable security procedures can be applied to new dataflows or data sources with ease.
When you think of your dataflows as pipelines, you can scale them up gradually. You can get started early and achieve value immediately by starting with a modest controllable slice from a data source to a user.
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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