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What is ETL?

ETL stands for extract, transform and load.

Most important role in nearly every business operation is data, which to be most usable and valuable you need a way to collect it from miscellaneous sources like source systems, organize it together and centralize it into another area for analysis or operational system for support of the organizations business process. So the complete process of extracting the data, transforming the data and loading the data into warehouse is called extract, transform and load.

What is ETL?

It is not a one-time process for loading the data to warehouse because the source data in the operational systems keeps on changing so this is a regular process, where the data has been regularly fetched from the source system and loaded into a warehouse after transformations are complete.

ETL has three phases – first is the extraction path, second is the transformation and third is the loading of the extractor and the transform data into the warehouse. Let’s take a look into each of these three stages in detail.

Extract

There are a couple of data extraction strategy:

  • Full Extract – all the data from the operational systems and all the source systems gets extracted into a staging area. The full extract requires keeping a copy of the last extract in the same format in order to be able to identify changes.
  • Partial Extract – sometimes we get the notification from the source systems that which data has been updated, which has been deleted and which data is the new data; so this is also called the Delta and in this strategy we only extract the data which has been modified and it is easy and quick as compared to the full expression.
  • Incremental Extract – with this strategy we do not extract the full data set from the system. When source systems are unable to provide notification that an update has occurred, but they can identify which records were modified, and provide an extract of only those records; one thing we need to know while extracting the data from source system is that it should be designed in a way it does not negatively affect the system performance because source system is generally where all the actual business and transaction happens, so we should design or schedule our system to run at that time that it should not negatively affect the system performance.
Extract process image

Transform

The data extracted into a staging server from the source system is in a raw format and we can’t use that data as it is. Therefore it needs to be cleansed, mapped and transformed as to the requirement before it is finally ready for loading into a Data Warehouse. So let’s look at the regular transformation tasks:

  • Selection – we select the data which is required to be loaded into the Data Warehouse.
  • Matching – we will look up the data from various local files and then match the data that needs to be transformed.
  • Cleansing – the data is not cleans and standardized in our source systems because we are fetching the data from more than one source system so it has to be standardized or normalized.
  • Consolidation or Aggregation – we consolidate and aggregate the data from the source system, because we do not want to load same data from system into warehouse, hence we consolidate summarize and aggregate the data from the system.
  • Splitting or merging fields – we split and merge fields; split it from a single field to multiple fields and then we combine some fields as well and this all happens based on the requirements.
  • Standardizing Data – as mentioned earlier data has been taken from various sources and then it needs to be standardized before loading into a warehouse.
  • Handling Character Encodings and Conversions – we also need to convert the data into a define encoding in the Data Warehouse because the system may or may not have a seam encoding.
  • Calculate and derive values – new data is created from existing (detailed) source data during this process by calculations, program logic or table lookups.
  • Conversion of units – we convert units because the system may have a different way of storing it, a particular measurement but we convert into a standard format.
  • De-duplication – is also done as a part of transformation in which we Identifying and removing duplicate records or duplicate columns from multiple sources.
  • Key restructuring – finally we design our Data Warehouse using surrogate key. Surrogate Key (SK) is sequentially generated meaningless unique number attached with each and every record in a table in our Data Warehouse.

Load

Loading stage involves the prepared data into the target from staging server and applies it to the Data Warehouse, Data Lake, or analytics application. There are 3 types of loading strategies we use.

  • Initial Load – when we load the data for the very first time we do not care about identifying the newly or the modified records; we generally take the whole data set from the staging server and load it into it Data Warehouse; so this is the one up process and it is generally done when the data is going to be populated with on the for the very first time.
  • Incremental Load – this is when we apply all the ongoing changes from the system into the Data Warehouse periodically; so in this we only load the records which have either changed or the newly inserted course into systems.
  • Full refresh – we sometimes required to do a full refresh as well which is basically completely erasing the contents of one or more tables and loading the fresh data; for certain tables we do want to keep the history of changes so it is better if we completely erase those tables and load all the data which is extracted from systems.
Load process image

Well Known ETL Tools

There is a list of the most popular ETL Tools with comparison on the market.

  • Improvado – data analytics software will allow you to connect marketing API to any visualization tool and for that no need to have technical skills. You will be able to connect and manage these data sources through one platform in the cloud or on-premises.
  • Skyvia – is a commercial, subscription-based cloud solution free plans available by Devart. includes an ETL solution for various data integration scenarios with support for CSV files, databases (SQL Server, Oracle, PostgreSQL, MySQL), cloud Data Warehouses (Amazon Redshift, Google BigQuery), and cloud applications (Salesforce, HubSpot, Dynamics CRM, and many others). It also includes a cloud data backup tool, online SQL client, and OData server-as-a-service solution.
  • IBM – Infosphere Information Server – It is mainly designed for Big Data companies and large-scale enterprises. It is a commercially licensed tool. Real-time data integration across multiple systems for all data types. It also helps to automate business processes for a more cost-saving purpose.
  • HEVO – is an enterprise-grade data pipelines as a service. With Hevo you can move data in Real-time from any of your Sources to any Destination without writing any code. Hassle-free, code-free ETL. No ETL Script maintenance or Cron jobs required. Point and Click Interface that allows moving data from any source to any Data Warehouse in minutes.
  • Xplenty – a cloud-based ETL solution providing simple visualized data pipelines for automated data flows across a wide range of sources and destinations. Xplenty has a new feature called Field Level Encryption, which allows users to encrypt and decrypt data fields using their own encryption key.
  • and much more like Oracle Data Integrator, Informatica – PowerCenter, IRI Voracity, Ab Initio, Talend, Pentaho, CloverDX, Apache Nifi.
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What are the key features and capabilities of ETL tools

  • The ability to connect to and extract data from a variety of sources – databases of all stripes (relational, NoSQL, etc.), big data systems built around technologies, flat file repositories, application-to-application message queues, and more.
  • Team-based development capabilities for collaborating on integration projects, with associated version control and release management features.
  • A GUI-based design environment that supports drag and drop development of source to target mappings and ETL workflows.
  • Data quality and cleansing functionality for identifying and fixing errors in data sets, plus data synchronization for keeping data consistent in source and target systems.
  • Capabilities for a job scheduler, along with process management controls that provide things like runtime monitoring and error alerting, handling, and logging. Which allow us to assign the jobs to particular users and which allows multiple developers to work simultaneously on the same project.
  • It should have connect us easily to get with Web Services ETS who should allow and give the capability to perform the operations in badge as well as the real time.

Final Thoughts

Data is important for businesses to make the critical Business Decisions. ETL tool plays a considerable role in validating and covering that the Business Information is Exact, Persistent and Reliable. Also, it minimizes hazard of data loss in production.

Related Reading: Best practices Data Warehouse

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