The best ETL solutions for businesses

Large business companies collect, keep, and process varied kinds of data from different sources, such as payroll systems, sales records, inventory systems, and others. This information is retrieved, converted, and transferred to data repositories by using etl solutions for businesses. Let’s tell you about ETL and its benefits in detail!

What does ETL generally mean?

So, literally, ETL means Extract, Transform, and Load. It is the process to combine data from multiple systems into a single data warehouse. Imagine a retailer with retail and online stores. He needs to analyze sales trends both online and offline. But backend systems are likely to be separate for them. They can have different fields or field formats for data collection, and use systems that cannot communicate with each other.

Luckily, there is an efficient solution, like Visual Flow ETL. The ETL system extracts data from both systems, converts it according to the data warehouse format requirements, and then uploads it to the repository.

The scheme always looks like the following: 

1. First, data is extracted from one or more sources.
2. Then it is prepared for integration.
3. After it is loaded and the extracted data goes into a common database.

The modern use of ETL

Modern ETL tools collect, convert and store data from millions of transactions in a variety of data sources and streams. 

This feature provides many new opportunities: 

• historical record analysis to optimize the sales process; 
• real-time price and stock adjustment; 
• machine learning and artificial intelligence to create predictive models; 
• developing new income streams; 
• moving into the clouds, and more.

Cloud migration

The process of migrating data and applications to the cloud is called cloud migration. It helps save money, make apps more scalable and protect data. ETL is then used to move data into the cloud.

Data Warehouse 

A data warehouse is a database that transmits data from various sources so that it can be jointly analyzed for commercial purposes. Here, ETL is used to move data into the data warehouse.

Machine learning

Machine learning is a method of data analysis that automates the construction of analytical models. ETL can be used to move data into a single machine-learning repository.

Integration of marketing data

Marketing integration involves moving all marketing data (about customers, sales, social networks, and web analytics) into one place so you can analyze them. ETL is used to integrate marketing data.

Integration of IoT data

There are lots of data collected by various sensors, including built-in equipment. ETL helps you move data from different IoT to one place so you can do a detailed analysis.

Database replication

The data from the original databases are copied to cloud storage. It can be a one-time operation or a continuous process when your data is updated in the cloud immediately after the update in the source database. ETL can be used to implement the data replication process.

Business Analyst

Business analysis is a data analysis process that enables managers, managers, and other stakeholders to make informed business decisions. You can use ETL to move your data to a single location so that you can use it. By the way, Visual Flow provides efficient and user-friendly ETL solutions for businesses.

ETL integration

To generate XML conversion files, TRIRIGA uses either the ETL Tivoli Directory Integrator Configuration Editor or the Pentaho ETL Development Environment Spoon. When run through an API, these transformations move data from the source tables to the destination tables.

ETL Integration Architecture

In TRIRIGA, two ETL environments are used to create ETL scripts that fill in fact tables. These two ETL development environments are the Tivoli Directory Integrator configuration editor and the Pentaho Spoon data integration tool. ETL development environments allow the creation of SQL queries that read data from TRIRIGA Business Object tables and display and convert results into fact and measurement columns of fact tables.

The ETL integration process

To move data from the source tables to the destination tables, you must run the ETL conversion files you have developed in the Tivoli Directory Integrator or Pentaho Spoon configuration editor via the API.

Configuration of necessary components for ETL integration

As part of the TRIRIGA Application Platform, the transformation is managed by the business object and form. To create a transformation, source tables and destination tables must be defined and maps prepared.

Identification and support of ETL transformations

Use the ETL development environment to create a transformation to move data. With this conversion, you can perform calculations and use variables from the TRIRIGA Application Platform and the system.

Start the ETL transformation

Use the TRIRIGA Task Scheduler to run ETL task elements and task groups to move data to TRIRIGA fact tables or too flat hierarchical tables.

Configure Conversion Objects

TRIRIGA contains ETL job elements and conversion objects. Instead of defining a new transformation object, you can configure an existing ETL job item transformation object. If you are using an existing transform object, you must define and support the transform. However, it is not necessary to define and maintain business objects, forms, and tasks of the workflow, as they are already defined.

Example of using ETL systems

Companies often store data in several independent systems. To synchronize data from multiple sources, they need the help of ETL.

For example, if two retailers combine their businesses, they may have several common suppliers, partners, and consumers. In addition, they may have data on all these facilities in their respective repositories. However, both parties may use different databases and data may not always match.

In this scenario, two companies can combine their databases into one using an ETL system. It, in turn, removes duplicates, standardizes formats, and synchronizes data.

To conclude

Traditional local ETLs are most often supplied with a headache. For example, they are built in-house, so they may quickly become obsolete or lack complex functions and capabilities. They are expensive and require maintenance time, support only batch processing, and are poorly scaled.

Local ETL platforms have been a critical component of enterprise infrastructure for decades. With the advent of cloud technologies, SaaS, and big data, the number of sources of information has increased, resulting in a growing demand for more powerful and complex data integration.

Modern ETL solutions for business have special requirements: 

• real-time data reception and their enrichment; 
• the ability to handle billions of transactions; 
• high-quality data support. 

In addition, these tools should be scalable, flexible, and secure.