Steps to Build ETL Pipeline: A Comprehensive Guide

• October 21st, 2020


In this blog, we will discuss the method to build ETL pipelines so that you can use them to perform ETL operations on your data. Here’s the detailed list that you’ll be covering in this blog.

Table of Contents

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Introduction to ETL

ETL is an abbreviation for Extract, Transform, and Loading. With the introduction of cloud technologies, many organizations are migrating their data from legacy source systems to cloud environments by using ETL tools. They often have data storage as an RDBMS or legacy system which lacks performance, and scalability. Hence, to get better performance, scalability, fault-tolerant, and recovery systems, organizations are migrating to cloud technologies like Amazon Web Services, Google cloud platform, Azure, private clouds, and many more.

In a typical industrial ETL scenario, ETL is an automated process that extracts data from legacy sources by using connectors for analysis, transforms them by applying calculations like filter, aggregation, ranking, business transformation, etc. that serves business needs, and then loads onto the target systems which is typically a data warehouse.  Below schematics will give a better understanding of ETL flow.


ETL Pipeline and Its Significance

ETL pipeline consists of tools or programs that extract the data from the source, transform it based on business needs, and load it to the output destination such as database, data warehouse, or data mart for further processing or reporting.

The schematics of ETL pipeline is as shown below –

ETL Pipeline

Significance of ETL Pipeline

  1. ETL pipeline clubs the ETL tools or processes and then automates the entire process, thereby allowing you to process the data without manual effort.
  1. ETL pipeline provides the control, monitoring and scheduling of the jobs.
  1. ETL pipeline tools such as Airflow, AWS Step function, GCP Data Flow provide the user-friendly UI to manage the ETL flows.
  1. ETL pipeline also enables you to have restart ability and recovery management in case of job failures.

Methods to Build ETL Pipeline

There are several methods by which you can build the pipeline, you can either create shell scripts and orchestrate via crontab, or you can use the ETL tools available in the market to build a custom ETL pipeline.

ETL pipelines are broadly classified into two categories – Batch processing and Real-time processing. Let’s deep dive on how you can build a pipeline for batch and real-time data.

Build ETL Pipeline with Batch Processing

In a traditional ETL pipeline, the data is processed in batches from the source systems to the target data warehouses. There are several tools that you can use to build ETL pipelines for your data. We have crafted a list of the best available ETL tools in the market based on the source and target systems that may help you to choose the best-suited one. You can find them here –

Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.

Below are the high-level steps that you might need to follow when building an ETL pipeline with batch processing :

  • Step 1. Create reference data: Reference data are data that contain the static references or permissible values that your data may include. You might need the reference data while transforming the data from source to target. However, this is an optional step and can be excluded if there is no need.
  • Step 2. Connectors to Extract data from sources: To establish the connection and extract the data from the source, you need the connectors or the defined tools that create the connection. The data can be of API, RDBMS, XML, JSON, CSV, and any other file formats. You need to extract all and convert it into a single format for standardized processing.
  • Step 3. Validate data: After extracting the data, it is essential to validate the data to check if they are in the expected range and reject them if not. For example, you need to extract the data for the past 24 hours, and you will reject the data that will contain the records older than 24 hours.
  • Step 4. Transform data: Once you validate the data, transformations include de-duplication of the data, cleansing, standardization, business rule application, data integrity check, aggregations, and many more.
  • Step 5. Stage data: This is the layer where you store the transformed data. It is not advisable to load transformed data directly into the target systems. Instead, the staging layer allows you to roll back the data easily if something goes wrong. The staging layer also generates Audit Reports for analysis, diagnosis, or regulatory compliance.
  • Step 6. Load to data warehouse: From the staging layer, the data is pushed to target data warehouses. You can either choose to overwrite the existing information or to append the data whenever the ETL pipeline loads a new batch.
  • Step 7. Scheduling: This is the last and most important part of automating your ETL pipeline. You can choose the schedule to load daily, weekly, monthly, or any custom range. The data loaded with the schedules can include a timestamp to identify the load date. Scheduling and task dependencies have to be done carefully to avoid memory and performance issues

Build ETL Pipeline with Real-time Stream Processing

Many sources like social media, e-commerce websites, etc. produce real-time data that requires constant transformations as it is received. You cannot perform ETL on these data in batches; instead, you need to perform ETL on the streams of the data by cleaning and transforming the data while it is in transit to the target systems.

There are many real-time stream processing tools available in the market, such as Apache Storm, AWS Kinesis, Apache Kafka, etc. Below diagram illustrates the ETL pipeline built on Kafka.

ETL pipeline built on Kafka

To build a stream processing ETL pipeline with Kafka, you need to:

  • Step 1. Data Extraction: The first step that you need to do is extract data from the source to Kafka by using Confluent JDBC connector or by writing custom codes that pull each record from the source and then write into Kafka topic. Kafka automatically pulls up the data whenever new records are generated and pushes to the topic as a new message enabling a real-time data stream.
  • Step 2. Pull data from Kafka topics: The ETL application extracts the data from the Kafka topics either in JSON or in AVRO format, which then deserializes to perform transformations by creating KStreams.
  • Step 3. Transform data: Once you pull the data from Kafka topics, you can do the transformation on KStream objects by using Spark, Java, Python, or any other programming language. The Kafka streams process each record at a time and produce one or more outputs depending upon the transformation built.
  • Step 4. Load data to other systems: After the transformation, the ETL application loads the streams into target applications such as data warehouses or data lakes.

Top ETL Tools

There are a lot of tools available in the market that can perform ETL and build ETL pipelines to automate this process. Some of the popular tools are listed below for your reference.

1. Hevo Data

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Hevo Data, a No-code Data Pipeline, helps to transfer data from 100+ sources to your desired data warehouse/ destination and visualize it in a BI tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Check out what makes Hevo amazing:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.

Hevo Data Use Case

Hevo provides a seamless data pipeline experience to companies. Hevo supports pre-built integration with 100+ data sources and allows data migration in real-time. With its ETL, ELT, and data transformation capabilities, you will always have analysis-ready data.

Pricing Model of Hevo Data

Hevo Pricing Plan
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Hevo Data provides users with three different subscription offerings, namely Free, Starter, and Business Plans, you can learn more about Hevo Data’s pricing here.

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2. AWS Glue

AWS Glue is a fully managed and cost-effective serverless ETL (Extract, Transform, and Load) service on the cloud. It allows you to categorize your data, clean and enrich it, and move it from source systems to target systems.

AWS Glue uses a centralized metadata repository known as Glue Catalog, to generate the Scala or Python code to perform ETL and allows you to modify and add new transformations. It also does job monitoring, scheduling, metadata management, and retries.

3. GCP Cloud Data Fusion

GCP’s Cloud Data Fusion is the newly introduced, powerful, and fully managed data engineering product. It helps users to build dynamic and effective ETL pipelines to migrate the data from source to target by carrying out transformations in between.

4. Apache Spark

Apache Spark is an open-source lightning-fast in-memory computation framework that can be installed with the existing Hadoop ecosystem as well as standalone. Many distributions like Cloudera, Databricks, GCP have adopted Apache Spark in their framework for data computation.

5. Talend

Talend is a popular tool to perform ETL on the data by using its pre-built drag and drop palette that contains pre-built transformations.

6. Apache Airflow

Apache Airflow is an open-source workflow automation and scheduling platform that programmatically author, schedule, and monitor workflows. Organizations use Airflow to orchestrate complex computational workflows, create data processing pipelines, and perform ETL processes.

Airflow uses DAG (Directed Acyclic Graph) to construct the workflow, and each DAG contains nodes and connectors. Nodes connect to other nodes via connectors to generate a dependency tree.


In this blog post, we guided you through the structured approach on what is ETL and how you can build an ETL pipeline. We have also listed top ETL tools that might help you to develop your customized ETL process and pipeline.

Visit our Website to Explore Hevo

However, if you’re looking for a more straightforward solution, you can use Hevo Data – a No Code Data pipeline that you can use to build an ETL pipeline in an instant. It has pre-built integrations with 100+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. Sign Up for the 14-day free trial to give Hevo a try.

Share your thoughts on building an ETL pipeline in the comments below!