How to implement ETL steps for a Data Warehouse?

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The world of data has been growing at an exponential rate, and the data management industry has changed dramatically in recent years. Approximately 90% of the current data has been generated in the last few years. According to a Domo report, our daily continuous data output is nearly 2.5 quintillion bytes, implying that massive amounts of data are generated every minute. Data has become a critical factor in business success as a result of technological advancements. Above all, properly processing data has become a critical solution for many businesses all over the world.

Most people would have thought terms like data lake, ETL (Extract, Transform, Load), or warehousing were just buzzwords a few years ago.

Data management technology is rapidly evolving today, providing organisations with numerous opportunities. There is a lot of raw data in today’s organisations that needs to be filtered. It’s a real challenge to organise data in a systematic way so that decision-makers can get actionable insights. As a result, useful data speeds up decision-making, and ETL tools for data management can be beneficial.

Introducing the modern data warehouse solution pattern with Azure SQL Data Warehouse

The evolution of Extract Transform Load (ETL)

To get actionable insights from all of your business data, data warehouses and ETL tools were created. Several ETL tools with expanded functionality for data cleansing, data profiling, big data processing, master data management, data governance, and Enterprise Application Integration are currently available on the market (EAI). A Business Intelligence (BI) software is used to visualise and analyse data stored in a warehouse or an Online analytical process (OLAP) cube. Reporting, data discovery, mining, and boarding are all made easier with this software.

The complete process

• An ETL process is the process of extracting and organising raw data, transforming it to make it understandable, and loading it into a database or data warehouse so that it can be easily accessed and analysed. In a nutshell, it’s an important part of any modern business’s data ecosystem.Because data from different sources has different structures, each dataset must be transformed differently before it can be used for business intelligence and analytics. If you’re organising data from Google Analytics and Amazon Redshift, for example, these two sources should be treated separately throughout the ETL process.Implementing the ETL process in the data warehouse

  There are three steps in the ETL process:

1. Extract

This step entails extracting data from the source system and transferring it to the staging area. Any transformations can be performed in the staging area without affecting the source system’s performance. Also, restoring corrupted data copied directly from the source into the data warehouse database could be difficult. Before moving data into the data warehouse, users can validate it in the staging area.

Data warehouses should integrate hardware, database management systems, operating systems, and communication protocols. Legacy apps, such as custom applications and mainframes, POC devices, such as call switches and ATMs, text files, ERP, spreadsheets, and data from partners and vendors are all sources.

Data can be extracted using one of three methods:

1. Full extraction
2. Partial extraction- with notification
3. Partial extraction- without notification

Data extraction should have no effect on the performance and response time of the source systems that make up the live production database, regardless of the method used. Any locking or sluggishness could have a negative impact on the company’s bottom line.

Validations during extraction

• • Records must be reconciled with the source data.
  • Checking records for spam or unwanted information
  • Identifying the data type
  • Data that is fragmented or duplicated is removed.
  • Making sure the keys are in the right place

2. Transform

The data obtained from the source server is insufficient and unusable in its current state. As a result, you’ll need to clean it up, map it out, and transform it. This is the most important step in the ETL process, where the data is enhanced and altered to produce intuitive BI reports.

You apply a set of functions to the data you’ve extracted in the second step. Pass-through data, also known as direct move, is data that does not require any transformation. You can also perform custom operations on data. For example, if a user wants total sales revenue, which isn’t in the database, or if the first and last names in a table are in separate columns, they can be combined in one column before loading.

There are some issues with data integrity as well:

1. Two people with the same name but different spellings (such as Nik versus Nick)
2. There are a variety of ways to represent a company’s name (like Adobe versus Adobe Inc.)
3. The names of various locations are used (like Cleveland and Cleaveland)
4. Through an application, different account numbers can be generated for the same customer.
5. Collection of invalid products at the point of sale due to a human error

Validations during transformation

• Filtering enables you to load only specific columns.
• Lookup tables and rules are used to standardise data.
• Character set conversion and encoding handling
• Currency, numerical, and date/time conversions are all examples of measuring unit conversions.
• Validation of data thresholds is being checked. The date of birth, for example, cannot be more than 11 digits long, including spaces.
• Data flow from the staging area to the intermediate tables is validated.
• A mandatory field marked with an asterisk should not be left blank.
• Mapping gender female to “F,” male to “M,” or null to “0” is an example.
• Creating a single column by dividing a column into multiple columns and combining them.
• Columns and rows are swapped.
• For data integration, lookups are used.
• Validation of any compound data.

3. Load

The final step in the ETL process is to load data into the data warehouse’s target database. Large volumes of data must be loaded in a relatively short period of time in a standard data warehouse. As a result, performance demands that the loading process be streamlined.

If a load fails, the recovery mechanism can be configured to restart from the point of failure without losing data integrity. Admins should keep an eye on the load and adjust it as needed based on the server’s performance.

Types of Load

• Initial load: This includes all of the data warehouse’s tables.
• Loading in stages: This type allows you to make ongoing changes as needed.
• Complete re-energize: Reloads one or more tables with new data after erasing the contents of one or more tables.

Load verification process

• The data in the key field should not be missing or set to null.
• Modelling views are being tested in accordance with the target tables.
• Checking the totals and coming up with calculated measures
• In the history table and the dimension table, there are data checks.
• The dimension table and the loaded fact are checked by BI reports.

Some prominent ETL tools

There are a variety of ETL tools on the market. Here are a few of the most significant:

Microsoft – SQL Server Integrated Services (SSIS): SSIS is a data migration software programme. It’s used to solve complex business problems by creating enterprise-level data transformations and data integration solutions. SSIS only supports SQL Server because it is a Microsoft product.

IBM InfoSphere Information Server:

IBM InfoSphere Information Server is a leading data integration platform that aids organisations in comprehending, cleansing, transforming, and delivering authentic and context-rich data.

Amazon Redshift:

Amazon Redshift is a data warehouse and Internet hosting service that is part of the larger AWS cloud computing platform. Using existing BI tools and standard SQL, this cost-effective and simple tool aids in the analysis of all types of data.

Oracle GoldenGate:

Oracle GoldenGate is a software package that allows for real-time data integration and replication in a variety of IT environments. High-availability solutions, transactional change data capture, data transformations, and data verification between analytical and operational enterprise systems are also possible.

Informatica PowerCenter:

Informatica PowerCenter is an ETL (extract, transform, load) tool for creating enterprise data warehouses. PowerCenter, as a high-performance and scalable tool, enables organisations to connect and fetch data from a variety of sources, as well as data processing.

The importance of ETL for businesses

There are numerous reasons to incorporate the ETL process into your business. Here are a few of the most important advantages:

Enhanced business intelligence

Embracing the ETL process will vastly improve the ease with which you can access your data. It assists you in retrieving the most pertinent datasets while making a business decision. The decisions you make in business have a direct impact on your operational and strategic tasks, giving you an advantage.

Substantial return on investment

It’s not easy to manage large amounts of data. You can organise data and make it understandable using the ETL process without wasting resources. With its assistance, you can put all of the collected data to good use and increase your return on investment.

Performance scalability

You must advance your company’s resources and technology in order to keep up with changing business trends and market dynamics. You can use the ETL system to layer the latest technologies on top of the infrastructure, simplifying the data processes that result.

Conclusion

Every company on the planet, whether small, medium-sized, or large, has a large amount of data. This data, however, is useless unless it is collected using a reliable method. ETL in data warehousing provides decision-makers with a complete picture of your business. The process is flexible and agile, allowing you to load data quickly, transform it into useful information, and use it for business analysis.