Extract, Transform, Load (ETL) is a critical process for businesses that prioritize data-driven insights. With the exponential growth of data sources and types, building and maintaining reliable data pipelines has become one of the more challenging parts of data engineering.
In this blog, we will discuss how to build an ETL pipeline so that you can use it to perform ETL operations on your data. We’ll also be covering 5 tools you can use to build etl pipelines so that you can focus on lever-moving tasks.
Table of Contents
What is 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.
It uses connectors for analysis, transforms them by applying calculations like a filter, aggregation, ranking, business transformation, etc. that serve business needs, and then loads them onto the target systems which is typically a data warehouse.
Below schematics will give a better understanding of ETL flow.
Hevo offers a faster way to move data from databases or SaaS applications into your data warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications. Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc can change, creating and managing these pipelines can be a soul-sucking exercise.
Hevo’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes.
All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software in terms of user reviews.
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What is ETL Pipeline?
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 a database, data warehouse, or data mart for further processing or reporting.
The schematics of the ETL pipeline is as shown below –
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Significance of ETL Pipeline
- ETL pipeline clubs the ETL tools or processes and then automates the entire process, thereby allowing you to process the data without manual effort.
- ETL pipeline provides the control, monitoring, and scheduling of the jobs.
- ETL pipeline tools such as Airflow, AWS Step function, and GCP Data Flow provide a user-friendly UI to manage the ETL flows.
- ETL pipeline also enables you to have restart ability and recovery management in case of job failures.
How to Build ETL Pipeline?
There are several methods by which you can create etl pipelines, 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 into 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 design 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 –
Below are the high-level steps that you might need to follow when building an ETL pipeline with batch processing :
- Create Reference Data: Reference data are data that contain 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.
- 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.
- 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 records older than 24 hours.
- 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.
- 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.
- 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 append the data whenever the ETL pipeline loads a new batch.
- 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. The following diagram illustrates the ETL pipeline built on Kafka.
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To build a stream processing ETL pipeline with Kafka, you need to:
- Data Extraction: The first step that you need to do is to extract data from the source to Kafka by using the Confluent JDBC connector or by writing custom codes that pull each record from the source and then write it into the Kafka topic. Kafka automatically pulls up the data whenever new records are generated and pushes it to the topic as a new message enabling a real-time data stream.
- 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.
- 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.
- Load Data to Other Systems: After the transformation, the ETL application loads the streams into target applications such as data warehouses or data lakes.
What are the Top Tools for Building ETL Pipelines?
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 150+ 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:
- Wide Range of Connectors: Instantly connect and read data from 150+ sources, including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
- In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty Python interface. Generate analysis-ready data in your warehouse using Hevo Data’s Postload Transformation
- Near Real-Time Replication: Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.
- Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with the destination warehouse so that you don’t face the pain of schema errors.
- Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spending. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
- 24×7 Customer Support: With Hevo, you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
- Security: Discover peace with end-to-end encryption and compliance with all major security certifications, including HIPAA, GDPR, and SOC-2.
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Hevo Data Use Case
Hevo provides a seamless data pipeline experience to companies. Hevo supports pre-built integration with 150+ 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
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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 a 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, and 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.
Conclusion
In this blog post, we guided you through the structured approach to what is ETL and how you can build an ETL pipeline. We have also listed the top ETL tools that might help you to develop your customized ETL process and pipeline.
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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 150+ 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 and give Hevo a try.
Share your thoughts on building an ETL pipeline in the comments below!