What is ETL and How does it Work?

The emergence of centralized data stores in the 1970s gave birth to ETL. But it wasn't until the late 1980s and early 1990s, when data warehouses became popular, that purpose-built tools to assist with data loading into these new warehouses were available. 

 

Data had to be "extracted" from siloed systems, "transformed" into the target format, and "loaded" by early adopters. The original ETL tools were crude, but they served their purpose. Granted, by today's standards, the quantity of data they processed was insignificant.

 

Data warehouses expanded in size as the amount of data rose, and ETL software tools multiplied and got more complex. However, until the late twentieth century, data storage and transformation were mostly done in on-premises data warehouses. But then something happened that forever changed the way we thought about data storage and processing.

 

ETL (extract, transform, and load) is a data integration process that integrates data from several sources into a single, consistent data store that is then put into a data warehouse or other destination system.

 

ETL was established as a procedure for integrating and loading data for calculation and analysis as databases became more popular in the 1970s, eventually becoming the dominant method for processing data for data warehousing initiatives.

 

Data analytics and machine learning work streams are built on top of ETL. ETL cleanses and organizes data using a set of business rules to meet particular business intelligence objectives, such as monthly reporting, but it can also handle more complex analytics to improve back-end operations or end-user experiences.

 

History of ELT

 

Organizations began employing numerous data repositories, or databases, to store diverse types of corporate information in the 1970s, and ETL grew in popularity. 

 

The demand to combine data from these disparate databases expanded rapidly. ETL became the industry standard for converting data from many sources before loading it into a target source, or destination.

 

Data warehouses first appeared in the late 1980s and early 1990s. Data warehouses, a special sort of database, allowed users to access data from a variety of sources, including mainframe computers, minicomputers, personal computers, and spreadsheets. 

 

Distinct departments, on the other hand, frequently employ separate ETL tools with different data warehouses. Many firms ended up with numerous separate ETL systems that were not integrated as a result of mergers and acquisitions.

 

The number of data types, sources, and systems has grown dramatically over time. Organizations currently employ a variety of ways to gather, import, and analyze data, including extract, convert, and load. Both ETL and ELT are critical components of a company's overall data integration strategy.

 

Also Read | 10 Types of Data Visualization

 

ETL vs ELT

 

The most noticeable distinction between ETL and ELT is the sequencing of operations. Instead of transferring the data to a staging area for transformation, ELT loads the raw data straight to the target data store, where it will be modified as needed.

 

While both methods employ a range of data repositories, including databases, data warehouses, and data lakes, they each have their own set of benefits and drawbacks. 

 

ELT is especially effective for large, unstructured datasets since it allows for direct loading from the source. Because data extraction and storage do not require significant advance planning, ELT may be a better fit for large data management.

 

The ETL method, on the other hand, needs greater upfront definition. Specific data points, as well as any relevant "keys," must be established for extraction and integration across diverse source systems. Even once that task is accomplished, the data transformation business rules must be built. 

 

This effort is typically dependent on the data requirements for a certain form of data analysis, which will define the extent of data summarization required. While the introduction of cloud databases has boosted ELT's popularity, it comes with its own set of drawbacks, such as the fact that best practices are still being defined.
 

 

How ETL Works?

 

Understanding what happens in each phase of the process is the simplest approach to grasp how ETL works.

 

1. Extract

 

Raw data is transferred or exported from source locations to a staging area during data extraction. Data management teams may extract information from a number of structured and unstructured data sources. Among these include, but are not limited to:

 

• SQL or NoSQL servers
• CRM and ERP systems
• Flat files
• Email
• Web pages

 

2. Transform

 

The raw data undergoes data processing in the staging area. The data is converted and consolidated in this step to prepare it for its intended analytical use case. 

 

The following tasks may be included in this phase:

 

• The data is filtered, cleansed, de-duplicated, validated, and authenticated.
• Using raw data to do computations, translations, or summarizations. Changing row and column headings for uniformity, converting currencies or other units of measurement, modifying text strings, and more are all examples of this.
• Audits are carried out to guarantee data quality and compliance.
• Removing, encrypting, or safeguarding data that is regulated by industry or government
• To meet the schema of the destination data warehouse, the data is formatted into tables or connected tables.

 

3. Load

 

The converted data is transported from the staging area to the target data warehouse in this final stage. This usually entails a full load of all data, followed by periodic loading of incremental data updates and, less frequently, full refreshes to wipe and replace data in the warehouse. 

 

The process is automated, well-defined, continuous, and batch-driven in most enterprises that employ ETL. ETL is often performed during off-peak hours, when traffic on the source systems and the data warehouse is at a minimum.

 

Also Read | Data Profiling

 

 

Why is ETL Important?

 

For many years, businesses have depended on the ETL process to obtain a consolidated picture of data that allows them to make better business choices. This approach of combining data from many systems and sources is still used today as part of a company's data integration toolkit. Here is the importance of ETL Tools :

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Why ETL is Important?

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1. ETL is a technique for moving and transforming data from a variety of sources and loading it into various destinations, such as Hadoop.

 

2. ETL gives rich historical context for the company when utilized with an enterprise data warehouse (data at rest).

 

3. ETL makes it easier for business users to examine and report on data relevant to their objectives by offering a consolidated perspective.

 

4. ETL has changed over time to accommodate new integration needs, such as streaming data.

 

5. To bring data together, ensure accuracy, and offer the audits often necessary for data warehousing, reporting, and analytics, organizations require both ETL and ELT.

 

How ETL Is Being Used?

 

Data quality, data governance, virtualization, and metadata are all components of data management that core ETL and ELT technologies deal with. Today's popular applications include:

 

1. Traditional ETL Applications

 

ETL is a tried-and-true approach that many businesses use on a daily basis, such as merchants that need to view sales data on a regular basis or health care providers who require an accurate representation of claims. 

 

ETL may integrate and expose transaction data from a warehouse or other data source so that it's available to see in a manner that business users can comprehend. 

 

Data migration from ancient systems to current systems with diverse data formats is also done using ETL. It's frequently used to combine data from mergers and acquisitions, as well as to acquire and link data from external suppliers or partners.

 

2. Transformations and Adapters for ETL with Big Data

 

Whoever collects the most data is the winner. While this isn't always the case, having fast access to a wide range of data can help organizations gain a competitive advantage. 

 

Businesses nowadays require access to a wide range of big data sources, including videos, social media, the Internet of Things (IoT), server logs, geographical data, open or crowdsourced data, and more. 

 

To satisfy these evolving requirements and new data sources, ETL suppliers routinely introduce new transformations to their systems. Data integration tools interact with adapters to extract and load data quickly. 

 

Adapters provide access to a wide range of data sources, and data integration tools interface with these adapters to extract and load data efficiently.

 

3. ETL for Hadoop

 

ETL has progressed to offer integration across a broader range of applications than traditional data warehouses. Structured and unstructured data may be loaded and converted into Hadoop using advanced ETL technologies. 

 

These tools read and write numerous files from and to Hadoop in parallel, streamlining how data is combined into a single transformation process. 

 

Some Hadoop-based systems include libraries of prebuilt ETL transforms for both transaction and interaction data. Transactional systems, operational data stores, BI platforms, master data management (MDM) hubs, and the cloud are all supported by ETL.

 

4. ETL and Self-Service Data Access

 

Self-service data preparation is a fast-growing trend that gives business users and other nontechnical data professionals the ability to access, mix, and convert data. Because this technique is ad hoc, it improves organizational agility and relieves IT of the responsibility of providing data in various forms to business users. 

 

There is less time spent preparing data and more time spent developing insights. As a result, both business and IT data professionals may increase their productivity, and businesses can expand their data-driven decision-making.

 

5. ETL and Data Quality

 

Data integrity is ensured through ETL and other data integration software solutions, which are used for data cleansing, profiling, and auditing. ETL tools interface with data quality tools, and ETL suppliers include related tools, such as data mapping and data lineage, in their packages.

 

6. ETL and Metadata

 

The lineage of data (where it originates from) and its influence on other data assets in the organization are both aided by metadata. As data infrastructures get more complicated, it's critical to keep track of how your organization's various data pieces are utilized and connected. 

 

If you add a Twitter account name to your customer database, for example, you'll need to determine which ETL operations, apps, or reports would be affected.

 

Also Read | Data Validation: Types Benefits and Drawbacks

 

ETL and Business Intelligence

 

Today there is heavy integration of ETL in business intelligence processes and systems which rely heavily on ETL technologies. It's the IT process of combining data from several sources into a single location, such as a data warehouse, in order to analyze and uncover business insights programmatically. 

 

When data isn't distributed across various digital sites, analysts have easier access to it. One of the most important advantages of ETL is the reduction of data silos.

 

ETL tools also help to improve the data quality for analytics. Data is more clean, accurate, and ready for business intelligence activities after going through the transformation process. Performing BI processes on erroneous or invalid data, on the other hand, puts your company at danger of making bad judgments.

 

It can also lead to poor customer relationship decisions, such as reaching out to leads at the incorrect time, as well as future compliance issues caused by erroneous data storage.

 

Data from many databases and other sources may be consolidated into a single repository containing data that has been correctly structured and qualified in preparation for analysis using ETL. 

 

This single data repository makes it easier to retrieve data for analysis and further processing. It also ensures that all enterprise data is consistent and up-to-date by providing a single source of truth.