Computer science is worlds away from where it was decades ago when computers had bulk monitors and took a moving team to move from one team to another. Smart technology is all around us, and it’s all due to data science. As you can imagine, it takes massive amounts of data to perform many of the functions artificial intelligence and automation enable. Naturally, this requires a wide range of data sources.
Data integration projects allow data scientists to choose data sources and extract data from different data sources and combine it all for processes ranging from analytics to creating algorithms. Extract, transfer, load (ETL) is one of the premier data integration strategies. This article will talk a little about ETL, and the tools data scientists use for this necessary process.
ETL is one of several data integration strategies. As mentioned in the introduction, it’s an acronym for extract, transfer, and load, a data integration process and set of tools.
ETL processes enable companies to integrate data from legacy systems, the internet, CRM, supply chain data, and other sources. Careful data governance and excellent communication among integration teams are central to completing ETL processes successfully.
ETL tools are what enable the extraction and transformation of raw data and data from legacy systems. During the data extraction phase, the integration engineer chooses the source systems to pull data. They can also take it a step further and look in each source system for specific data.
After choosing the data, the data manager will begin extracting it from various sources and migrating it to a staging area. The staging area is where your integration team earns their paychecks. They have to cleanse data for accuracy and format it for uniformity.
The staging area is where your integration team earns their paychecks. They have to cleanse data for accuracy and format it for uniformity. They also have to ensure no duplicate data while backing up each step of their workflows to ensure they don’t lose anything during data migration. In other words, it’s the stage of ETL processes in which you transform data.
One of the purposes of transforming data is to have a universal language for our insights from disparate systems. It also gives you a chance to remove redundancies and ensure data quality. Data engineers can also implement business rules to govern data insights and algorithms to performs certain functions without human interference. Indeed, the transformation step is full of possibilities.
The loading process is the final step of ETL integrations. During this step, the data manager loads the newly structured data into the target destination. This destination can be a data warehouse or data mart. Once the data is cleansed, transformed, and in the data warehouse, it’s ready for business users to employ for analytics and other data operations.
Data integration is central to connecting business users and governments with the insights that connect them to their respective worlds. Big data is all around us, whether it’s finding out a customer’s favorite genre of movies or metering internet bandwidth to match peak usage times. Data integration enables companies to harness the power of big data to help them make better decisions and gain a competitive edge.
ETL solutions give data scientists and business users absolute autonomy over the integration process. They can formate their data to various languages, including Python and SQL. They can apply business rules to different datasets and use data cleansing to ensure data quality. ETL processes are laborious, but learning ETL could improve your career prospects significantly.