Conventional on-premise ETL solutions come bundled with a set of migraines. Usually built in-house, such tools are complicated, fragile, time-consuming, and expensive. They work on batch processing principle as opposed to real-time processing, and so can quickly become obsolete.
Modern ETL tools, on the other hand, can seize, transform, and load data from a plethora of transactions across a variety of data sources and streams to provide a wealth of new opportunities: moving information to the cloud, enabling lightning-speed analysis of historical records to optimize the sales process, adjusting prices and inventory in real-time, improving productivity, and developing new revenue streams.
But what is Modern ETL and how does it stay relevant even in today’s time? For better understanding, we have enlisted a stream of frequently asked questions and their answers about modern ETL tools.
ETL, extract, transform, load, is a common paradigm that involves combining data extracted from multiple systems to a single database warehouse or data repository for legacy storage or analytics.
ETL process includes 3 important steps - extraction, transformation, and loading.
Extraction, the first phase of the ETL process, is the process of retrieving data (structured and unstructured) from myriad sources, including:
· Existing databases and legacy systems
· Cloud, hybrid, and on-premises environments
· Sales and marketing applications
· Mobile devices and apps
· CRM systems
· Data storage platforms
· Data warehouses
· Analytics tools
After the retrieval process, ETL tools load data straight into a staging area and prepare it for the next phase i.e. transformation.
Generally considered as the most important phase, transformation paves way for integration. It is the segment of the ETL process where rules and regulations are applied to the extracted data to ensure data quality as well as accessibility. ETL Transformation phase includes many sub-processes such as cleansing, deduplication, sorting, verification, standardization etc.
Cleansing: In this process, missing values and inconsistencies in data are detected and resolved.
Deduplication: This is the process where redundant data is discarded.
Sorting: In this process, data is organized or sorted according to the required format.
Verification: Data is verified in this process. Unusable data is eliminated and anomalies are flagged.
Standardization: In this process, formatting rules are applied to the data.
Apart from this, the transformation phase may also include some additional tasks where rules are applied to improve data quality further.
Data transformation not only enhances data integrity but also ensures that data travels safely to its new destination.
Loading is the final step of the ETL process where the newly transformed data is loaded into a new destination. The transformed data can be loaded either all at once or at scheduled intervals.
Depending on how and where you want to transform the data, ETL solutions can be of several types:
ETL data integration tools can be used for various functions such as:
Moving data from a source database to a target data repository with the help of ETL tools has a number of benefits.
All set to see what a modern ETL platform can do for your business? Get started today!