ETL pipeline refers to a set of processes which extract the data from an input source, transform the data and loading into an output destination such as datamart, database and data warehouse for analysis, reporting and data synchronization.

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ETL stands for Extract, Transform, and load.

Extract

In this stage, data is extracted from various heterogeneous sources such as business systems, marketing tools, sensor data, APIs, and transaction databases.

Transform

The second step is to transform the data into a format that is used by different applications. In this stage, we change the data from the format where the data was stored in the format used in the different applications. After successful extraction of the data, we will convert the data into a form which is used for standardized processing. There are various tools used in the ETL process, such as Data Stage, Informatica, or SQL Server Integration Services.

Load

This is the final phase of the ETL process. Here, the information is available in consistent format. Now we can obtain any specific piece of data and can compare it to another part of data.

Data Warehouse can either be automatically updated or manually triggered.

These steps are performed between warehouses based on the requirements. Data is temporarily stored in at least one set of staging tables as part of the process.

However, the data pipeline will not end when the data is loaded to the database or data warehouse. ETL is currently growing so that it can support integration across the transactional systems, operational data stores, MDM hubs, Cloud and Hadoop platform. The process of data transformation is become more complicated because of the growth in the unstructured data. For example, modern data processes include real-time data such as web analytics data from extensive e-commerce website. Hadoop is synonym with big data. Several Hadoop-based tools are developed to handle the different aspects of the ETL process. The tools we can use depend on how the data is structured, in batches or if we are dealing with streams of data.

Difference between ETL Pipeline and Data Pipeline

Although the ETL pipeline and data pipeline pretty much do the same activity. They move the data across platforms and transforming it in the way. The main difference is in the application for which the pipeline is being built.

ETL Pipelines

ETL pipeline is built for data warehouse application, including enterprise data warehouse as well as subject-specific data marts. ETL pipeline is also used for data migration solution when the new application is replacing traditional applications. ETL pipelines are generally built by using industry-standard ETL tools that are proficient in transforming the structured data.

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Data pipelines or business intelligence engineers build ETL Pipelines.

Data Pipelines

Data Pipelines can be built for any application that uses data to bring value. It can be used for integrating the data across applications, build the data-driven web products, build the predictive models, create real-time data streaming applications, carrying out the data mining activities, building the data-driven features in digital products. The use of the data pipeline is increased in the last decade with the availability of the open-source big data technology, which is used to build data pipelines. These technologies are capable of transforming the unstructured as well as structured data.

Data engineers build data Pipelines.

Differences between the ETL Pipeline and Data Pipeline are:

ETL Pipeline	Data Pipeline
ETL pipeline defines as the process of extracting the data form one system, transforming it and loading it into some database or data warehouse.	Data Pipeline refers to any set of processing elements that moves the data from one system to another and transforming the data along the way.
ETL pipeline implies that the pipeline works in batches. For example- pipe is run once every 12 hours.	Data Pipeline can also be run as a streaming evaluation (i.e., every event is handled as it occurs). Type of data pipeline is an ELT pipeline (loading the entire data to the data warehouse and transform it later).

ETL FILES

ETL files are log files which have been created by Microsoft Tracelog software applications. Microsoft program creates the event logs in the format of a binary file. In a Microsoft operating system, kernel created the logs. ETL logs contain the information about how to access the disk and page fault, recording the performance of the Microsoft Operating System, and logging the event of high-frequency.

The Eclipse Open Development Platform also uses the .etl file extension. The platform creates the file which is saved with the .etl file extension.

Trace logs are generated by trace provider in trace session buffer and are stored by the operating systems. Trace logs are then written to a log and stored in a compressed binary format to reduce the amount of space. From ETL files, reports may be generated using the command line utility Tracerpt. The output of the ETL file may be configured with several options such as the maximum allowable size of the file so that the logs do not cause a computer run out of disk space.

The ETL file type is associated with the Eclipse foundation. Eclipse is an open-source community whose projects are focused on building a free development platform comprised of extensible.

ETL files stored to disk, and changes in their volatility and the data they contain. When a trace session is configured first, then the used settings determine how to store the log files and what data to be stored in them. Some logs are circular with old data which is overwritten with new data when the size of the file reached to the maximum. Windows stores the information into ETL files in some scenarios such as when the system is shut down, booted when another user has logged into the system, when the updating occurs or many more.

Microsoft office, one drive, sky drive, and skype can also maintain their ETL files which contain the debugging and other information. The information in the ETL file can be used in forensics for a variety of scenario.

ETL File Location

In a window system, ETL files can be found anywhere. These files exist on most of the system and can contain a lot of information and that information is used for analysis. ETL files can be found in different locations in windows operating system, and maybe hundreds of them are empty, and some is containing the data.

So, this brings us to the end of blog. This Tecklearn ‘Concept of ETL Pipeline and Files’ blog helps you with commonly asked questions if you are looking out for a job in ETL. If you wish to learn Data Warehousing and build a career in ETL domain, then check out our interactive, ETL Testing Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/etl-testing-training/

ETL Testing Training

About the Course

Today’s businesses have to work with data in multiple formats extracted from multiple sources. All this makes ETL Testing all the more important. Tecklearn’s ETL Testing training offers an in-depth understanding of Data warehousing and business intelligence concepts through real-world examples. You will also gain the essential knowledge of ETL testing, Performance Tuning, cubes, etc., through hands-on projects, and this will help you to become a successful ETL Testing expert.

Why Should you take ETL Testing Training?

An ETL Developer can earn $100,000 per year – indeed.com
Global Big Data Analytics Market to reach $40.6 Billion in 4 years.
Most companies estimate that they’re analysing a mere 12% of the available data. – Forrester Research

What you will Learn in this Course?

Introduction to ETL testing

Introduction to ETL testing
Life cycle of ETL Testing
Database concepts and ETL in Business Intelligence
Understanding the difference between OLTP and OLAP and data warehousing

Database Testing and Data Warehousing Testing

Introduction to Relational Database Management Systems (RDBMS)
Concepts of Relational database
Data warehousing testing Versus database
Integrity constraints
Test data warehousing testing
Hands On

ETL Testing Scenarios

Data warehouse workflow
ETL Testing scenarios and ETL Mapping
Data Warehouse Testing
Data Mismatch and Data Loss Testing
Creation of Data warehouse workflow
Create ETL Mapping
Hands On

Various Testing Scenarios

Introduction to various testing scenarios
Structure validation and constraint validation
Data correctness, completeness, quality and Data validation
Negative testing
Hands On

Data Checks using SQL

Using SQL for checking data
Understanding database structure
Working with SQL Scripts
Hands On

Reports & Cube testing

Reports and Cube Testing
Scope of Business Intelligence Testing
Hands On

Got a question for us? Please mention it in the comments section and we will get back to you.

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