The process of ETL (Extract, Transform, Load) plays a key role in data integration strategies. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.
A typical ETL process collects and refines different types of data, then delivers the data to a data warehouse such as Redshift, Azure, or Big Query.
ETL also makes it possible to migrate data between a variety of sources, destinations, and analysis tools. As a result, the ETL process plays a critical role in producing business intelligence and executing broader data management strategies.
What is ETL?
The mechanism of extracting information from source systems and bringing it into the data warehouse is commonly called ETL, which stands for Extraction, Transformation and Loading.
The ETL process requires active inputs from various stakeholders, including developers, analysts, testers, top executives and is technically challenging.
To maintain its value as a tool for decision-makers, Data warehouse technique needs to change with business changes. ETL is a recurring method (daily, weekly, monthly) of a Data warehouse system and needs to be agile, automated, and well documented.
How ETL Works?
ETL consists of three separate phases:
Extraction
Extraction is the operation of extracting information from a source system for further use in a data warehouse environment. This is the first stage of the ETL process.
Extraction process is often one of the most time-consuming tasks in the ETL.
The source systems might be complicated and poorly documented, and thus determining which data needs to be extracted can be difficult.
The data has to be extracted several times in a periodic manner to supply all changed data to the warehouse and keep it up-to-date.
Cleansing
The cleansing stage is crucial in a data warehouse technique because it is supposed to improve data quality. The primary data cleansing features found in ETL tools are rectification and homogenization. They use specific dictionaries to rectify typing mistakes and to recognize synonyms, as well as rule-based cleansing to enforce domain-specific rules and defines appropriate associations between values.
The following examples show the essential of data cleaning:
- If an enterprise wishes to contact its users or its suppliers, a complete, accurate and up-to-date list of contact addresses, email addresses and telephone numbers must be available.
- If a client or supplier calls, the staff responding should be quickly able to find the person in the enterprise database, but this need that the caller's name or his/her company name is listed in the database.
- If a user appears in the databases with two or more slightly different names or different account numbers, it becomes difficult to update the customer's information.
Transformation
Transformation is the core of the reconciliation phase. It converts records from its operational source format into a particular data warehouse format. If we implement a three-layer architecture, this phase outputs our reconciled data layer.
The following points must be rectified in this phase:
- Loose texts may hide valuable information. For example, XYZ PVT Ltd does not explicitly show that this is a Limited Partnership company.
- Different formats can be used for individual data. For example, data can be saved as a string or as three integers.
Following are the main transformation processes aimed at populating the reconciled data layer:
- Conversion and normalization that operate on both storage formats and units of measure to make data uniform.
- Matching those associates equivalent fields in different sources.
- Selection that reduces the number of source fields and records.
Cleansing and Transformation processes are often closely linked in ETL tools.
Loading
The Load is the process of writing the data into the target database. During the load step, it is necessary to ensure that the load is performed correctly and with as little resources as possible.
Loading can be carried in two ways:
- Refresh: Data Warehouse data is completely rewritten. This means that older file is replaced. Refresh is usually used in combination with static extraction to populate a data warehouse initially.
- Update: Only those changes applied to source information are added to the Data Warehouse. An update is typically carried out without deleting or modifying preexisting data. This method is used in combination with incremental extraction to update data warehouses regularly.
Selecting an ETL Tool
Selection of an appropriate ETL Tools is an important decision that has to be made in choosing the importance of an ODS or data warehousing application. The ETL tools are required to provide coordinated access to multiple data sources so that relevant data may be extracted from them. An ETL tool would generally contains tools for data cleansing, re-organization, transformations, aggregation, calculation and automatic loading of information into the object database.
An ETL tool should provide a simple user interface that allows data cleansing and data transformation rules to be specified using a point-and-click approach. When all mappings and transformations have been defined, the ETL tool should automatically generate the data extract/transformation/load programs, which typically run-in batch mode.
ETL Process
ETL is the process of extracting data from non-optimized data sources and moving it to a centralised host (which is). The particular steps in that procedure may alter with ETL tools, but the end outcome is the same.
The ETL process, in its most basic form, involves data extraction, transformation, and loading. While the abbreviation implies a clean three-step procedure – extract, convert, load — this simplistic explanation misses:
- The transmission of data
- The overlapping of each of these stages
Describe how new technologies are altering this flow.
Traditional ETL process
Data is taken from online transaction processing (OLTP) databases, sometimes known as 'transactional databases' nowadays, as well as other data sources. With a significant number of read and write requests, OLTP applications have a high throughput. They are not well suited to data analysis or business intelligence jobs.
In a staging area, data is then converted. These transformations address both data cleansing and data optimization for analysis. The converted data is subsequently imported into an online analytical processing (OLAP) database, sometimes known as an analytics database nowadays.
The data is then queried by business intelligence (BI) teams, who give the results to end users or individuals in charge of making business decisions, or it is utilised as input for machine learning algorithms or other data science projects. One common issue seen here is that if the OLAP summaries cannot accommodate the sort of analysis desired by the BI team, the entire process must be repeated, this time with alternative transformations.
Data Warehouse ETL process
For a variety of reasons, modern technology has altered most firms' approach to ETL.
The most significant is the introduction of strong analytics warehouses such as Amazon Redshift and Google Big Query. These newer cloud-based analytics databases are powerful enough to do transformations in place rather than requiring a separate staging location.
Another factor is the fast adoption of cloud-based SaaS apps, which now store considerable volumes of business-critical data in their own databases and are accessible via various technologies such as APIs and webhooks.
Furthermore, data is usually evaluated in raw form today rather than through preloaded OLAP summaries. As a result, lightweight, adaptable, and transparent ETL systems have been developed, with procedures that look somewhat like this:
The main advantage of this configuration is that transformations and data modelling take place in the analytics database, in SQL. This provides the BI team, data scientists, and analysts more control over how they interact with information in a language that everyone understands.
Components of ETL
Regardless of the ETL process you use, there are a few key components to keep in mind:
- Support for change data capture (CDC) (also known as binlog replication): Incremental loading enables you to update your analytics warehouse with fresh data without reloading the entire data set.
- Auditing and logging: Detailed logging inside the ETL pipeline are required to ensure that data can be audited after it has been loaded and that faults can be debugged.
- Handling different source formats: In order to pull data from diverse sources such as Salesforce's API, your back-end financials application, and databases such as MySQL and MongoDB, your process must be capable of handling a number of data types.
- Fault tolerance: Problems are unavoidable in any system. ETL systems must be able to recover gracefully, ensuring that data gets from one end of the pipeline to the other even if the first pass finds issues.
- Notification support:
- Proactive notification directly to end users when API credentials expire.
- Passing along an error from a third-party API with a description that can help developers debug and fix an issue.
- If there’s an unexpected error in a connector, automatically creating a ticket to have an engineer look into it.
- Utilizing systems-level monitoring for things like errors in networking or databases.
- Low latency: Some decisions need to be made in real time, so data freshness is critical. While there will be latency constraints imposed by particular source data integrations, data should flow through your ETL process with as little latency as possible.
- Scalability: As your company grows, so will your data volume. All components of an ETL process should scale to support arbitrarily large throughput.
- Accuracy: Data cannot be dropped or changed in a way that corrupts its meaning. Every data point should be auditable at every stage in your process.
