What is and How ETL Pipeline Works ? (Explained)
What is and How ETL Pipeline Works ? (Explained). Nowadays, with data playing an increasingly important role in business, data integration is becoming an essential part of an organisation’s success. One of the most important tools used for this purpose is the ETL pipeline. This process enables organisations to collect data from different sources, process it and load it into a target data store.
In this article on the ETL pipeline, we walk you through a basic overview of the pipeline process and also discuss the key steps in the ETL. You are going to learn about the benefits of using an ETL pipeline, what tools are used in the ETL process, and how to design and implement an effective ETL pipeline for your project.
What is an ETL Pipeline ?
An ETL pipeline is a process used as part of data integration that allows organisations to extract data from various sources, transform it and load it into a target data store. The ETL acronym stands for Extract, Transform, Load, which refers to the three main stages of the process. ETL pipelines are commonly used in data warehouses, business analytics and reporting projects. The aforementioned process helps organisations consolidate data from different sources and enables analysis and reporting.
Key steps in the ETL Process
The first step in the entire ETL Pipeline environment is to:
Extraction – The first move in the ETL process is the so called extraction. The data is extracted from various sources, which are similar formats, such as relational information databases. And they include NoSQL, XML, etc. Well, the data from different source systems is stored in a special area – a staging. Make sure no to keep it directly in the data warehouse, because the extracted data is often in different formats and might be corrupted. Since, loading it directly into a database can destroy it, and restoring it will be difficult.
Transformation – This step applies a set of rules or functions to the extracted data to convert it into a single standard format. This includes following processes/activities:
- Filtering – loads only certain attributes into the data warehouse.
- Cleansing – populates NULL with default values, map US, USA and America to USA, etc.
- Merge – combines multiple attributes into one.
- Splits one attribute into multiple attributes.
- Sorts tuples by some property (usually the property key).
Loading – The final and third step in the ETL process is loading. Transformed data in this step is finally loaded into the data warehouse. Some load the data into the data warehouse often, whilst others take longer but regular intervals. Loading speed and duration are completely dependent on your requirements and vary from system to system.
ETL Pipeline Tools
Here are some popular tools for building extract, transform and load (ETL) data pipelines.
1. Talend
Talend Open Studio (TOS) is one of the most important ETL tools for data integration on the market. From initial ETL design to execution of the ETL data load, TOS makes it easy to manage all stages involved in the ETL data pipeline process.
Using the graphical interface that Talend Open Studio offers, you quickly map structured/unstructured data from source to target systems. All you have to do is drag and drop the necessary components from the list into the workspace, configure them, and then link them. This provides access to a metadata library where you quickly use and reuse your work.
2. Apache Hive
Apache Hive software is a data warehousing solution and ETL tool built on the Hadoop platform for large scale data summarization, analysis, and querying. Apache Hive data warehouse software makes big data management and query execution easy. This allows semi structured and unstructured data to be transformed into schema based payloads. Hive software makes it easy to access and modify data in Hadoop for those who know SQL and work with standard DBMS databases.
3. AWS Glue
AWS Accurate ecosystem offers AWS Glue, a fully managed, cloud based ETL service. Fully managed ETL map that makes it easy to prepare data for analysis. The server based platform has a wide range of features that perform further tasks, such as AWS Glue Data Catalog for recognizing data in the enterprise space and AWS Glue Studio for dynamically creating, executing and managing ETL data pipelines.
Simply, AWS Glue application is easy to use. Just create and execute an ETL task in the AWS Administration Console with a few clicks. All you need to do is install AWS Glue to point to your data stored in AWS. The AWS Glue Data Catalog program automatically loads your data and associated metadata. Once this process is complete, your data is immediately available and accessible to the ETL data pipeline.
ETL Pipeline Use Cases
By transforming raw data into fits for the target system, ETL pipelines enable systematic and accurate data analysis in the target repository. So, from data migration to faster insights, ETL pipelines are imperative for data driven enterprises. They save time and effort for data teams by eliminating errors, bottlenecks and delays to ensure a smooth flow of data from one system to another. Here are some of the basic use cases.
- Enables data migration from the legacy system to the new repository.
- Enriches data from one system, such as a CRM platform, with data from another system, such as a marketing automation platform.
- Provides data analysis tools with a stable data set for quick access to one predefined analytic use case when the data set is already structured and transformed.
- Centralization of all data sources to obtain a unified version of the data.
- Complies with GDPR, HIPAA, and CCPA standards with the understanding that users can omit sensitive data before uploading it to the target system.
Benefits of using ETL Pipeline
Improved decision making
Business decisions are increasingly data driven, especially in marketing. Whether using data to develop new strategies or analyzing campaign performance to understand its effectiveness, marketing initiatives are closely tied to numbers and data.
Moreover, data pipelines driving the stream of data while collecting it in a central repository, hence easier to drill down. So, the quality of your decisions depends not only on the quality of the data, but also on the quality of the analysis itself.
Data quality
As data flows through the pipeline, it is refined and cleaned, making it more meaningful and useful to end users. Because data pipelines standardize reporting processes, they ensure that all data is processed and collected consistently. This ensures that the data in your reports is reliable and accurate. You may forget inconsistent date ranges for metrics, copy paste errors and mistakes in Excel formulas that hamper your agency’s operations.
Standardization
Data Normalization transforms raw data into a common and homogeneous form that allows analysts and other business users to analyse and draw useful conclusions from it. It delivers full and comprehensive data catalogue to have a deep understanding of how the data has been transformed. Essential to ensure reliability, compliance and security. Normalize your data with the following steps:
Set data standards – Find data sets to be normalized and how they shall be normalized.
Understanding data sources – Know where your incoming data comes from. Understanding data reporting assists with identifying data standardization challenges that data scientists may face.
Clean up raw data – Make sure the data is checked and formatted correctly.
Best practices for building ETL pipelines
ETL Logging
Preserve the processing pipelines the best with of ETL is ETL logging. It captures all events before, throughout and after the ETL process. Each business is very unique and requires a custom solution. You can’t pick one ETL process that fits all. Remember to keep proper logs.
Improve the Quality of your Data
Make sure that the data you input into ETL processing is as accurate as possible to get the most reliable and fast output. For example, the automatic data validation feature helps with this task by finding missing and inconsistent data in the data set.
Reduce Data Input
Remember not to always use the sequential ETL approach. Instead, you implement as many ETL integrations as your architecture supports to minimize time to value. Less data input into the ETL process results in faster and cleaner output. Please remove all unnecessary data from your ETL processing pipeline as quickly as possible.
Checkpoint Restore
Remove all unnecessary data as early as possible in the ETL processing pipeline. The output is faster and cleaner if you introduce less data into the ETL process.
What is and How ETL Pipeline Works ? Conclusion
In summary, the ETL pipeline is an important component of today’s data architecture that enables companies to collect, transform and load data from various sources to a destination for analysis and decision making. Additionally companies looking for a way to harness the power of data in decision making should consider implementing an ETL pipeline in their data architecture to collect, transform and load data in an efficient manner.
