How to implement the ETL steps for your data warehouse?
The world of data has been growing exponentially, and the data management industry is totally changed from what it was a few years ago. Around 90% of the current data has been generated in the last couple of years only. According to a report by Domo, our continuous data output is nearly 2.5 quintillion bytes in a day, which means there’s massive data generated every minute. With technological transformation, data has become a critical factor in business success. Above all, processing data in the right way has become a pivotal solution for many businesses around the globe.
If we look back, terms like a data lake, ETL (Extract, Transform, Load), or warehousing would have been baffling for most people, or merely some buzzwords.
Today, data management technology is growing at a fast pace and providing ample opportunities to organizations. Organizations these days are full of raw data that needs filtering. Systematically arranging the data to get actionable insights for decision-makers is a real challenge. Thus, meaningful data accelerates decision-making, and using ETL tools for data management can be helpful.
The evolution of Extract Transform Load (ETL)
Data warehouses and ETL tools were created to get actionable insights from all your business data. There are currently several ETL tools in the market that have expanded functionality for data cleansing, data profiling, big data processing, master data management, data governance, and Enterprise Application Integration (EAI). With the availability of data in the warehouse or Online analytical process (OLAP) cube, a Business Intelligence (BI) software is used to visualize and analyze them. This software helps with reporting, data discovery, mining, and boarding.
The complete process
The process of extracting and organizing raw data, transforming to make it understandable, and loading it into a database or a data warehouse to easily access and analyze it, is known as an ETL process. In short, it’s an essential component in the data ecosystem of any contemporary business.
Since data coming from various sources has a distinct structure, every dataset needs to be transformed differently before using it for business intelligence and analytics. For example, if you organize data from source systems like Google Analytics and Amazon Redshift, these two sources should be treated separately with the whole ETL process.
Implementing the ETL process in the data warehouse
The ETL process includes three steps:
This step comprises data extraction from the source system into the staging area. Any transformations can be done in the staging area without degrading the performance of the source system. Also, if you copy any corrupted data directly from the source into the database of the data warehouse, restoring could be a challenge. Users can validate extracted data in the staging area before moving it into the data warehouse.
The data warehouses should merge systems with hardware, DBMS, OS, and communication protocols. Sources include legacy apps like custom applications, mainframes, POC devices like call switches, ATM, text files, ERP, spreadsheets, data from partners, and vendors. As a result, you need a logical data map before extracting data and loading it physically. The data map represents the connection between sources and target data.
There are three methods of extracting data:
- Full extraction
- Partial extraction- with notification
- Partial extraction- without notification
Despite the method used, data extraction shouldn’t have any impact on the performance and response time of the source systems that are the live production database. Any locking or slowdown could affect the company’s bottom line.
Validations during extraction
- Reconciliation of records with the source data
- Verifying records for spam or unwanted data
- Checking data type
- Removal of fragmented/duplicate data
- Checking the placement of keys
The data that is extracted from the source server is incomplete and not usable in its original form. Because of this, you need to cleanse, map, and transform it. This is the most important step where the ETL process enhances and alters data to generate intuitive BI reports.
In the second step, you apply a set of functions on the data that you’ve extracted. Data that doesn’t need any transformation is called pass-through data or direct move. Also, you can execute custom operations on data. For example, if a user wants total sales revenue, which is not present in the database, or if the first and last name in a table is in separate columns it’s possible to integrate them in the same column before loading.
There are also some issues in data integrity:
- Different spellings of two persons having the same name (such as Nik versus Nick)
- Different ways of denoting a company name (like Adobe versus Adobe Inc.)
- Use of different places’ names (like Cleveland and Cleaveland)
- Generation of different account numbers for the same customer through an application
- Collection of invalid products at Point of Sales due to a mistake in a manual entry
Validations during transformation
- Filtering – To select only specific columns for loading.
- Data standardization using lookup tables and rules.
- Encoding handling and character set conversion.
- Conversion of measuring units like currency, numerical, and date/time conversions.
- Checking data threshold validation. For instance, the date of birth can’t be more than 11 digits, including spaces.
- Validation of data flow from the staging area to the intermediate tables.
- Required field with an asterisk sign shouldn’t be left blank.
- For example, mapping gender female to “F”, male to “M”, or null to “0”.
- Dividing a column into multiple columns and combining multiples into a single column.
- Interchanging columns and rows.
- Using lookups for data integration.
- Using any compound data validation.
The last step of the ETL process includes loading data into the target database of the data warehouse. In a standard data warehouse, large volumes of data have to be loaded in a comparatively short period. As a result, the loading process needs to be streamlined for performance.
If there’s any load failure, one can configure the recovery mechanism to restart from the point of failure without losing data integrity. Admins should monitor, resume, and cancel the load according to the server performance.
Types of Load
- Initial load: Includes all the tables of the data warehouse
- Incremental load: In this type, you can apply ongoing changes when needed from time to time
- Full refresh: Erases the contents of one or more tables and reloads it with new data
Load verification process
- The key field data should neither be missing nor set to null
- Testing of modeling views as per the target tables
- Checking the combined values and creating calculated measures
- Data checks in the history table and dimension table
- BI reports check on the dimension table and the loaded fact
Some prominent ETL tools
There are various ETL tools available in the market. Here are some of the most important ones:
- Microsoft – SQL Server Integrated Services (SSIS): SSIS is a product developed for data migration. It’s used for building enterprise-level data transformations and data integration solutions to solve complex business problems. Being a product of Microsoft, SSIS only supports SQL Server.
- IBM InfoSphere Information Server: IBM InfoSphere Information Server is a leading data integration platform that helps organizations understand, cleanse, transform, and deliver authentic and context-rich information.
- Amazon Redshift: Amazon Redshift is an Internet hosting service and a Data Warehouse tool that forms part of the bigger cloud-computing platform AWS. This cost-effective and simple tool helps analyze all types of data using existing BI tools and standard SQL.
- Oracle GoldenGate: Oracle GoldenGate is an extensive software package for real-time data integration and replication in diversified IT environments. It also allows high availability solutions, transactional change data capture, data transformations, and verification between analytical and operational enterprise systems.
- Informatica PowerCenter: Informatica PowerCenter is an ETL tool used to build enterprise data warehouses. Being a high-performing and scalable tool, PowerCenter enables organizations to connect and fetch data from diversified sources and processing of data.
The importance of ETL for businesses
There are many reasons to include the ETL process within your organization. Here are some of the key benefits:
Enhanced business intelligence
Embracing the ETL process will radically improve the level of accessing your data. It helps you pull up the most relevant datasets while you make a business decision. The business decisions have a direct impact on your operational and strategic tasks and give you an upper hand.
Substantial return on investment
Managing massive volumes of data isn’t easy. With the ETL process, you can organize data and make it understandable, without wasting your resources. With its help, you can put all the collected data to quality use and make way for a higher return on investment.
With evolving business trends and market dynamics, you need to advance your company’s resources and the technology it uses. With the ETL system, you can add the latest technologies on top of the infrastructure, which simplifies the resulting data processes.
Every business around the world, whether small, mid-sized, or large, has an extensive amount of data. However, this data is nothing without using a robust process to gather it. Implementing ETL in data warehousing provides a full context of your business for the decision-makers. The process is flexible and agile that allows you to swiftly load data, transform it into meaningful information, and use it to conduct business analysis.
Transforming your contracts and enterprise data into strategic business assets