ETL stands for Extract, Transform, and Load. Many organizations are adopting this to migrate their legacy data to the modern cloud technology that enables them to process data at a fast rate. In this blog, you will get to know what the ETL process is, the best way to ETL Data, and various factors that you need to keep in mind while performing ETL.
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What is ETL Data?
ETL is an abbreviation for Extract, Transform, and Load. 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 Data flow.
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ETL Process Steps: Way to ETL Data
The term ETL is not just Extract, Transform, and Load. It contains a lot of other parameters that you may need to keep in mind. Performing an ETL is a tedious task and has to be carried out carefully by keeping various factors in mind. With the introduction of ETL tools, there is some relief in terms of code writing, monitoring, etc. However, here is the step-by-step guide that will help you with ETL Data.
- Source Identification: The first and foremost step in the ETL Data is to identify the source and type of data that you want to extract. These include knowing data types, schemas, business process, domain, source (RDBMS, Legacy, Files), etc. that plays an essential factor when ETL is considered.
- Create Connections: Once you have identified the type of data and source, now you need to identify the connectors that can extract the data. You can either write the custom code or use the available ETL tools in the market to extract the data. The data can be in structured storage systems like RDBMS, data warehouse, or in files like JSON, XML, CSV, or other available modes.
Connectors can help you to extract the data efficiently and smoothly. You can also write custom codes if there are no connectors available to extract and parse the data.
- Data Extraction: Once you establish the connection successfully, the data extraction process begins, and the pipeline/code will continue to extract the data and dump it to the data warehouses or databases. The data can be of API, RDBMS, XML, JSON, CSV, and any other file formats and needs to be converted into a single format for standardized processing.
- Data Cleansing: Raw data are often uncleaned, non-standardized, and non-validated. You need to perform Data Profiling to get insights about the data and then set up the process to clean, standardize, and validate the data according to the business needs. You can clean the data before transforming, or you can apply some automated task that checks for predefined rules to adhere to data quality.
- Create reference data: Reference data is data that contains 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.
- Establish Logging: Logging is an important aspect, and you need to set up a logging framework to correctly log the status of the jobs, count of records, data of executions, etc. Logs will help you to understand how the process is behaving and pin-points the errors and any bottlenecks.
- 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.
- Data Load: 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.
- Creating Checkpoint: It is always advisable to create checkpoints when performing ETL on the data sets. Unexpected errors are widespread when you migrate to large datasets. Creating a checkpoint will help you to validate the smaller groups and enable rollback in case of failures. Checkpoint also helps in restarting the process from the point of failure rather than starting from scratch.
- Scheduling: This is the last and most important part of automating your ETL Data 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.
Hevo Data is a No-code Data Pipeline that offers a fully-managed solution to set up data integration from 100+ data sources (including 30+ free data sources) and will let you directly load data to a Data Warehouse such as Snowflake, Amazon Redshift, Google BigQuery, etc. or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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- 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 the 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.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Limitations of Manual ETL
Now that we have seen how to ETL Data, there are certain limitations to these manual processes. Let’s have a look –
- The above methods describe the manual ETL Data process, and it takes months to set up the entire ETL Data pipeline.
- Data Extraction, Cleansing, Validation, and other business transformation requires coding and one should be well versed with the programming languages.
- Concerning Data Integrity and Data Security, you have to go an extra mile for implementation.
- Creating an ETL Data pipeline is a tremendous job as it requires the creation of various scripts, defining dependencies, and defining the flow of execution.
To overcome the above limitation, we have curated a list of the top ETL tools available in the market that can help you to set up your ETL Data pipeline quickly to ETL Data. Let’s have a look at the top ETL tools.
Top ETL Tools
To overcome the manual efforts, there are a lot of tools available in the market that can perform ETL and build ETL Data pipelines to automate this process. Some of the popular tools are listed below for your reference.
- Hevo Data
- AWS Glue
- GCP Cloud Data Fusion
- Apache Spark
1) Hevo Data
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.
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Stitch Data is a powerful tool for ETL Data that is built for developers and it can easily connect with the data sources to extract the data and moves to analysis very quickly. Stitch sets up in minutes without any hassle and provides unlimited data volume during the trial period.
3) 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 ETL Data and allows you to modify and add new transformations. It also does job monitoring, scheduling, metadata management, and retries.
4) 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 Data pipelines to migrate the data from source to target by carrying out transformations in between.
5) 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.
Talend is a popular tool for ETL Data by using its pre-built drag and drop palette that contains pre-built transformations.
In this blog post, you got to know the best way to ETL Data and the factors that you have to keep in mind while performing the ETL. However, if you’re looking for a more straightforward solution, you can use Hevo Data – a No Code Data pipeline to build ETL in an instant.
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Hevo offers a faster way to move data from 100+ data sources such as SaaS applications or Databases such as MySQL into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Have you used any of these methods? Have any further queries? Reach out to us in the comments section below.