Modern business analytics and intelligence are dependent on more than just data. Data and data pipelines are an integral part of an organization’s analytical structure. For this very reason, it is essential to understand how your data is sourced, transferred, stored, processed, and ultimately used for reporting and analysis purposes.
The efficiency of data analytics and business intelligence relies on how you extract, transform, and load your data. Therefore, a whole branch of data science engineering caters to how your business establishes a flawless data pipeline design. After all, a complementary pipeline design needs to fit your business-specific data models and analytics requirements like a gloved hand.
Before proceeding further, let’s take a quick peek into the nuances of designing data pipelines, it’s role and purpose within ETL operations.
What is ETL Data Pipeline?
A data’s path from its source to its destination is often known as an Extract, Load, and Transform or an ETL data pipeline.
Modern age businesses rely on digital processes to analyze large volumes of data. Such methods guide decisions, operations and facilitate the smooth functioning of data transference.
ETL data pipeline designs can scale up and down to meet business requirements instantaneously. These designs obtain valuable business intelligence from CRM, and social media, along with users and non-user-based web processes.
Here’s what makes an ETL pipeline essential within an organization:
- Facilitating real-time data reporting
- Analyzing real-time data in a dynamic environment
- Utilizing collected data to run business processes
Hence, market demands are at an all-time high for cloud-based and on-premises ETL pipelines. Most businesses prefer to set up their data pipelines with Python. This is regardless of the data’s sources, which can range from DBMSs to RDBMSs.
On the contrary, before hiring costly data pipeline specialists, businesses must learn why an ETL pipeline is necessary for their business. Only then can one implement a data pipeline architecture that generates a satisfactory ROI.
Why Build a Data Pipeline?
A data pipeline is a primary medium responsible for extracting data from various sources. The collected data is transformed using business logics and data models. Finally, it is loaded into a destination ‘database’ for further analysis and reporting.
Here’s why an organization should build an effective data pipeline for seamless data transfers:
- Consolidating data into readily-available, efficient models for analytical inferences
- Automate data transformation to reduce time consumptions and error margins associated with manual interventions
- Migrate smoothly from legacy systems to data warehouses
- Perform basic and advanced post-transformation analytics to facilitate deeper analytical insights
B2B data exchange pipelines deal with business statutory-compliant, transactional data. Quality pipelines are used for continuous operations such as day-to-day transformations, cleansing, standardization, and much more.
An exponentially growing market for ETL data pipeline tools simplifies the ETL process. By factoring in different business reasons, it is safe to say that businesses need to invest considerable thought and research before setting up a custom data pipeline.
An ETL pipeline consists of different constituents, each of which are listed below.
Designing a Data Pipeline
Before designing a data pipeline, you need to ask a few pertinent questions. These questions will lay the base for helping an organization design a pipeline to meet its business needs.
- How frequently will you be using your pipeline?
- What quality and quantity of data will your pipeline handle?
- Will your data be structured or unstructured?
- What technologies will the pipeline integrate? These technologies range from analytical, to data science, and business intelligence, automation, to machine learning.
Businesses must indulge in data pipelines that increase data accessibility and efficiently perform continuous data transformations. Such workflows should be easy to implement and maintain, thereby contributing to its ease of scalability.
Businesses must invest in a data pipeline architecture that generates differential, analytical intelligence and insights for competitive advantages.
For best results, data engineers should design a data pipeline in the following order:
1. Defining Data Sources
Earlier, businesses dealt with a singular data source. However, modern data engineering facilitates data sourcing from multiple sources, such as APIs, RDBMSs, Non-RDBMSs, the cloud, and many more.
Since the data is in its rawest form, the pipeline is only concerned with extracting the data from multiple sources and loading it into the pre-defined destinations. The whole transformation process is a unique requirement based on custom business logic.
Therefore, present-day data pipeline designs must always handle data in massive volumes, in real-time, or in batch systems.
A standardized raw data format offers faster basic data processing. The fundamental transformation of raw data into an operational, standardized form is called ingestion, which is the next step in the designing phase.
A data ingestion pipeline is the next logical step in setting up a pipeline after extraction. Data engineers must focus on three key areas that present primary challenges for data ingestion.
- Data validation, to ensure all usable data adheres to the same standardized format after extraction
- Fragmentation of ingested data, so that each form of data is easily used and ingested
- Integration with third-party data tools and tooling platforms for better reporting and analysis
Businesses should look into designing pipelines that can ingest any volume of unstructured data. This task becomes challenging when companies begin to scale.
Hence, engineers must implement measures to automate format standardizations upon extraction. Effective automation allows ingestion processes to run on an adhoc, scheduled, or customary basis.
Effective data pipeline automation can also help stave off problems with data fragmentation, irrespective of how the same forms of data are interpreted.
Additionally, cleansing allows us to format the data to facilitate custom transformation processes. Such compartmentalization procedures prevent duplication of operational data entries, thereby making the entire process smooth and seamless.
In layman terms, data cleansing is all about separating the chaff from the wheat. At times, your organization may also have to deal with missing or incomplete data. Alternatively, your existing data can become obsolete or be replaced by fresher datasets. Either way, you cannot provision unnecessary data as it only leads to space usage, wastage of existing resources and slowing down your in-house processes.
Your data cleansing strategies must cleanse the data directly at the source. Compartmentalizing data for cleaning it at its entry points prevents overlap and fragmentation. Such policies are easy to implement and can go a long way in helping you maintain the sanctity of your data and data sources.
Data cleaning processes, coupled with automation procedures, assist in making your data pipeline effective and efficient in the long run. The AWS data pipeline structure is an apt example of data cleansing, which gives organizations an excellent strategy for uploading, storing and managing clean data within repositories.
4. Transform & Analyze
Plain data migration pipelines do not always consist of a transformation step. However, ETL data transformations always rely on a transformation step.
Single-source, clean and structured data is first initiated and collected for transformation, before being loaded into data repositories. Further on, this multi-source data is used for data cleansing rituals.
On-premise ETL solutions offer a more hands-on approach towards end-to-end pipeline data. Cloud-based, virtualized ETL data pipelines are beneficial only when specific data is required.
Cleansing and transformation processes are often rendered as integrated steps so that the data for transformation is not exposed. Corrected data is then loaded into its destination, to be used in forward processes.
5. Define Destinations
The end destinations can range from a data warehouse, a database, a data lake, or even a silo. A data pipeline connects such repositories to transfer data from the source to the end destinations. Such repositories often include Snowflake, Redshift, AWS data lakes, and many more.
The destination also defines the nature of the pipeline. An example is the Facebook data pipeline, which continues to be a hybrid, real-time data pipeline used to transfer user data from one source to another.
You can design your pipeline to populate an ERP or CRM system as well. It can deliver the data to your vendor’s data repositories. Alternatively, you can create a complex data pipeline architecture to feed data into your intranet repositories for batch processing, custom standardization, de-duplication, and other related processes.
Most business ETL pipelines have a complex, multifaceted dashboard for their daily and long-term data operations. Data monitoring is the last step of the designing stage, which can help monitor data for patterns and inferences that suit data operation goals. AI-based monitoring casts a bird’s eye view over these metrics post-implementation.
Realistically, it is always advisable to aim for automation when managing large-scale hybrid data pipelines for enterprise-scale operations. They can process large batches of data overnight and also reduce errors in monitoring. Automated monitoring goes a long way in accelerating pipeline troubleshooting.
How Does Kloudio Help?
Many businesses do not realize that their ETL processes are only as performant as a data pipeline’s design itself. To achieve perfection in designing data pipelines, you have to begin at the grassroots level. You can easily escape the data analytics and BI challenges that emerge later with business growth through the implementation of such strategies.
To ease your pain of designing and managing a data pipeline, you can rely on organizations like Kloudio, which provide hassle-free data pipeline structures. Extract, transform and load your data from various repositories like Snowflake, Redshift, Azure, and many more, at the click of a button.
To know how Kloudio can help, delve deeper into Kloudio 2.0 for more information.