ETL- Extract Transform and Load

Data Integration Process

What is ETL ?

Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML). You can address specific business intelligence needs through data analytics (such as predicting the outcome of business decisions, generating reports and dashboards, reducing operational inefficiency, and more).

How ETL Works ?

Step 1: Extraction

Most businesses manage data from a variety of data sources and use a number of data analysis tools to produce business intelligence. To execute such a complex data strategy, the data must be able to travel freely between systems and apps.

Before data can be moved to a new destination, it must first be extracted from its source — such as a data warehouse or data lake. In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. Volumes of data can be extracted from a wide range of data sources, including:

• Existing databases and legacy systems
• Cloud, hybrid, and on-premises environments
• Sales and marketing applications
• Mobile devices and apps
• CRM systems
• Data storage platforms
• Data warehouses
• Analytics tools

Step 2: Transformation

During this phase of the ETL process, rules and regulations can be applied that ensure data quality and accessibility. You can also apply rules to help your company meet reporting requirements. The process of data transformation is comprised of several sub-processes:

• Cleansing — inconsistencies and missing values in the data are resolved.
• Standardization — formatting rules are applied to the dataset.
• Deduplication — redundant data is excluded or discarded.
• Verification — unusable data is removed and anomalies are flagged.
• Sorting — data is organized according to type.
• Other tasks — any additional/optional rules can be applied to improve data quality.

Transformation is generally considered to be the most important part of the ETL process. Data transformation improves data integrity — removing duplicates and ensuring that raw data arrives at its new destination fully compatible and ready to use.

Step 3: Loading

The final step in the ETL process is to load the newly transformed data into a new destination (data lake or data warehouse.) Data can be loaded all at once (full load) or at scheduled intervals (incremental load).

Full loading — In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, unique records in the data warehouse or data repository. Though there may be times this is useful for research purposes, full loading produces datasets that grow exponentially and can quickly become difficult to maintain.

Incremental loading — A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new and unique information is found. This architecture allows smaller, less expensive data warehouses to maintain and manage business intelligence.

ETL tool options

Although there are multiple ETL tools, not all are built for the modern data environment. Organizations need tools that are flexible and quick enough for the pace of business today. Ideally, they should also support a variety of use cases. Some of the ETL tools used throughout the data landscape today include:

• Incumbent or legacy ETL tools: These tools still provide core data integration functionality, but are slower, more brittle, and less flexible than contemporary options. Many of these tools are code intensive and lack the automation (especially for real-time deployments) compared to other selections.
• Open-source ETL tools: Open source ETL tools are a lot more adaptable than legacy tools are. They work on data of varying structures and formats — legacy tools basically work only on structured data. The open-source nature of these tools make them faster than most legacy tools.
• Cloud-based ETL tools: Cloud-based ETL tools make data readily available, and are flexible enough to account for the different structures associated with big data. Because of this flexibility, cloud-based ETL tools are more effective than on-premises options for dealing with hybrid cloud data source.

Some Alternatives to ETL Framework

1. Microsoft SQL Server : Linux and Docker containers for the first time ever, enabling developers to build intelligent applications using their preferred language and environment. Experience industry-leading performance, rest assured with innovative security features, transform your business with AI built-in, and deliver insights wherever your users are with mobile BI.
2. MuleSoft Anypoint Platform: Anypoint Platform™ is a complete platform that lets companies realize business transformation through API-led connectivity. It is is a unified, flexible integration platform that solves the most challenging connectivity problems across SOA, SaaS and APIs.
3. Cleo Integration Cloud:Cleo Integration Cloud is an ecosystem integration platform that makes it easy to build, automate and manage B2B, application, cloud, and data integrations. It’s scalable, intuitive, and requires no custom code or specialized skillsets to manage daily integration operations.
4. FiveTran : Fivetran is an ETL tool, designed to reinvent the simplicity by which data gets into data warehouses.
5. Supermetrics :Supermetrics helps over half a million marketers and analysts move data from popular marketing and ad platforms like Facebook, Google, and HubSpot to their favorite analytics, reporting, and data warehouse platforms. Over 15% of global ad spend is reported through Supermetrics