Data is essential in helping organizations build better strategies and make informed decisions in today's modern marketplace. It allows businesses to be strategic in their approaches, back up arguments, find solutions to everyday problems, and measure the effectiveness of a given strategy.
In addition to its advantages, managing and accessing data is a critical task with numerous undertakings. And it is where Data Pipeline proves to be most helpful.
Data Pipeline allows data scientists and teams to access data based on Cloud platforms. Data pipeline tools control data from a starting point to a specified endpoint. It enables you to collect data from many sources to create beneficial insights for a competitive edge.
Even the most seasoned data scientists can rapidly become overwhelmed by the significant data volume, velocity, and diversity. For this reason, organizations employ a data pipeline to turn raw data into analytical, high-quality information.
To deepen your understanding of the data pipeline, we have put together this definitive guide that covers its components, types, use cases, and everything.
Then this guide is for you. Let’s dive right in.
What is a Data Pipeline?
A data pipeline is a set of tools, actions, and activities implemented in a specific order to collect raw data from multiple sources and move it to a destination for storage and analysis. A data pipeline allows it to automatically gather data from several sources, alter it, and combine it into a single, high-performance data storage.
The five critical components of a data pipeline are preprocessing, storage, analysis, applications, and delivery. These five components help organizations work seamlessly with data and generate valuable insights.
Why Should You Implement a Data Pipeline?
A data pipeline helps you make sense of big data and transform it into high-quality information for analysis and business intelligence. Regardless of size, all businesses can use data pipelines to remain competitive in today's market. This data can be further utilized to understand client needs, sell goods, and drive revenue. Since it offers five essential elements, data pipeline integration plays a significant role in the process and enables businesses to manage enormous amounts of data.
Data is expanding quickly and will keep expanding. And as it happens, the data pipelines will help turn all the raw data into a daily stream of data. Applications, data analytics, and machine learning can all utilize this modified data. If you intend to utilize this data, you will require data integration, which requires a data pipeline.
Data Pipeline vs ETL
The phrases ETL and Data Pipeline are sometimes used interchangeably, but they differ slightly. ETL refers to extract, transform, and load. ETL pipelines are widely used to extract data from a source system, convert it following requirements, and load it into a Database or Data Warehouse mainly for Analytical purposes.
- Extract: Data acquisition and ingestion from original, diverse source systems.
- Transform: Transferring data in a staging area for short-term storage and transforming data so that it complies with accepted forms for other applications, such as analysis.
- Load: Loading and transferring reformatted data to the target drive.
Data pipeline, on the other hand, may be thought of as a more general phrase that includes ETL as a subset. It alludes to a system that is employed to transfer data across systems. There may or may not be any alterations made to this data. Depending on the business and data requirements, it may be handled in batches or in real-time. This information may be put into various locations, such as an AWS S3 Bucket or a Data Lake.
It may even activate a Webhook on another system to initiate a particular business activity.
Different Data Pipeline Components
A data pipeline consists of various components. All these data pipeline components have their technical requirements and challenges to overcome. You must first be familiar with the components of a data pipeline to comprehend how it functions. A typical data pipeline's structure resembles this:
In a data pipeline, the origin is where data is first entered. It is often driven by data store design. Most pipelines originate from storage systems like data warehouses, data lakes, transactional processing applications, social media, APIs, and IoT device sensors.
The ideal place for data should usually be where it makes the most sense to lower latency for near real-time pipelines or to optimize transactional performance/storage costs. If transactional systems deliver trustworthy and timely information required at the pipeline's destination, they should be regarded as an origin(s).
A destination is an endpoint to where data is finally transported to. The endpoint can be anything from a data warehouse to a data lake. It mostly depends on the use case. This is what the entire process is aimed for. When creating your data pipelines, you should start here.
Determining origin and destination go hand in hand, with endpoint needs affecting the discovery of data sources and data source selection influencing the choice of pipeline endpoints. For instance, it is necessary to consider both the timeliness requirements at the pipeline's destination and the latency restrictions at the pipeline's origin.
Origin and destination together determine what goes in the pipeline and what comes out, whereas dataflow is what specifies how data moves through a pipeline. In simple words, dataflow is the order in which processes and stores are utilized to transport data from a source to an endpoint. It refers to the transfer of data between points of origin and destinations as well as any modifications that are made to it. The three phases of dataflow are:
- Extract: It is defined as the process of extracting all essential data from the source. For the most part, the sources include databases such as MySQL, Oracle, MongoDB, CRM, ERP, and more.
- Transform: It is the process of transforming the essential data into a format and structure fit for analysis. It is done with a better understanding of data with the help of business intelligence or data analysis tools. It also includes activities like filtering, cleansing, validating, de-duplicating and authenticating.
- Load: It is the process of storing the converted data in the desired location. Loading is a term that is frequently used to describe data warehouses like Amazon Redshift, Google BigQuery, Snowflake, and others.
Storage refers to all the systems that are used to maintain data as it moves through the various stages of the data pipeline. The factors that influence the storage options available are the volume of data, how frequently and thoroughly a storage system is searched, and how the data will be used.
Processing refers to the steps and activities followed to collect, transform, and distribute data across the pipeline. Data processing, while connected to the dataflow, focuses on the execution of this movement. Processing converts input data into output data by completing the proper steps in the correct order. This data is exported or extracted during the ingestion process and further improved, enhanced, and formatted for the intended usage.
The workflow outlines the order of activities or tasks in a data pipeline and how they are interdependent. You will greatly benefit here if you know jobs, upstream, and downstream. Jobs refer to the discrete units of labor that finish a certain task, in this case changing data. The upstream and downstream refer to the sources and destinations of data traveling via a pipeline.
The main objective of monitoring is to examine the efficiency, accuracy, and consistency of the data as it moves through the various processing stages of the data pipeline and to ensure that no information is lost along the way. It is done to ensure that the Pipeline and all of its stages are functioning properly and carrying out the necessary tasks.
Watch this video from our Technical Lead - Business Intelligence, Balasubramanian Loganathan, to learn 8 important steps to build an optimal data pipeline.
Data Pipeline Architecture
Businesses are moving in the direction of implementing cutting-edge tools, cloud-native infrastructure, and technologies to better their business operation. This calls for transferring huge amounts of data. Automated Data Pipelines are essential elements of this contemporary stack that enable businesses to enrich their data, collect it in one location, analyze it, and enhance their business intelligence. This Modern stack includes:
- An Automated Data Pipeline tool
- A destination Cloud platform such as Databricks, Amazon Redshift, Snowflake, Data Lakes, Google BigQuery, etc.
- Business Intelligence tools like Tableau, Looker, and Power BI
- A data transformation tool
Ingestion of data, transformation and storage are the three main phases of the data pipeline architecture.
1. Data ingestion
To gather data (structured and unstructured data), several data sources are employed. Producers, publishers, and senders are common terms used to describe streaming data sources. Before collecting and processing raw data, it is always preferable to practice storing it in a cloud data warehouse. Businesses may also update any older data records using this technique to modify data processing tasks.
2. Data Transformation
In this stage, data is processed into the format needed by the final data repository through a series of jobs. These activities guarantee that data is regularly cleansed and transformed by automating repetitive workstreams, such as business reporting. In the case of a data stream in nested JSON format, for instance, key fields may be retrieved during the data transformation step from nested JSON streams.
3. Data Storage
Following storage, the altered data is made accessible to various stakeholders inside a data repository. Within streaming data, the transformed data are frequently referred to as customers, subscribers, or receivers.