As businesses onboard more cloud-based and digital services, they take in more and more data. And as facts grows, you have the possibility to analyze it to improve your enterprise. Data control can display wherein gaps want to be stuffed, how to enhance purchaser studies, and what your subsequent investments should be. But analyzing data to get this kind of visibility and insight is easier said than done.
That’s where Extract, Transform, Load (ETL) comes in. ETL is a process that extracts data from multiple sources, converts it into an interpretable form, and then loads it into a data warehouse or repository. As businesses embrace Big Data, they need ETL to make any sense of that data. As a method for processing large amounts of data into digestible sources of truth, ETL can help with reporting, improving processes, and supporting advanced analysis.
Various low-code ETL software platforms and solutions are available, but the right one will follow the best method for data analysis. Let’s take a look at the steps in that process:
Figure Out The Data Source
Identifying the data that needs extracting is a vital first step. Data characteristics can include whether it’s legacy or cloud data, and you’ll also need to identify what type of data it is and where it’s stored. Since ETL can gather all data — no matter the original language — and convert and load it, you don’t need to be concerned with its raw form. Anything from Microsoft Azure to JSON, Java, Hadoop, and Snowflake is fine.
Establish Data Extraction Connectors
Your ETL tool will need to be pre-built, or you will need customized coding for this step that requires identifying the connectors that will help extract data from pipelines without compromising quality. You might need specific connectors depending on the dataset you’re working with, and the right ETL solution will be able to build connectors for you.
Extract Data
In this step, your code will now begin the extraction process of your data in order to put it into your repository. Your data has not yet been converted or standardized, so it is still in its original file format.
Clean Data
The ETL process has now put your data into your warehouse and arrives in its original form — or what is known as “unclean” (clean data is standardized and validated). This step is where data profiling occurs, which is a process that validates and summarizes data to deliver a glimpse into its contents. From here, the ETL process will know how to clean the data so it can be converted.
Establish Data References
Your ETL tool might require setting specific parameters data must adhere to, known as data references, that assist in the data’s conversion from its source to your repository. If the tool does require it, this is where that step takes place – otherwise, it is unnecessary.
Create Logs
In this phase, you’ll want to establish logging frameworks that will give you important insight into your activities, enable you to record job status, executions, and record counts; and discover bottlenecks in your data or inaccuracies.
Validate Your Data
After extraction, validating your data involves ensuring individual data points are within your determined ranges and meet certain requirements. Reject any data that does not meet the criteria you’ve established.
Transform Your Data
The data transformation process converts the data from its raw form to one ready for staging and loading into your warehouse. This involves checking for duplicate data entries across your business, such as customer contact information, in multiple tools. You’ll distill these entries into one, functioning as another check for standardization and integrity. You should also check the data transformation tools review here (einblick).
You can convert data in either a multi-stage process or an in-warehouse process. The former is a method in which you move your data to a staging area after you extract it, and it gets converted before loading. The latter is necessary if you have no staging area and requires putting the “load” phase before the “transformation” phase in the ETL process.
For some transformation phases, you’ll extract more basic functions such as standardizing data lengths and types, mapping values, and establishing relationships between those values. In a more advanced transformation, you’ll employ field decoding, information merges, calculated or derived values, and summarizing data.
Load Your Data
Once you’ve determined that your data is validated and viable for analysis, it has reached the staging phase and is ready to load into its ultimate repository.
Establish Checkpoints
You’ll want to create checkpoints as a best practice for ensuring errors have not made their way into your migrated data — a common occurrence. Using checkpoints helps you double-check smaller data sets and make changes more nimbly instead of backtracking on larger data volumes. Checkpoints also allow you to resume loading your data from where you’ve fixed the problem — instead of starting from scratch.
Schedule Data Loading
This final step in ETL allows you to streamline the processing of your data for an indeterminate amount of time. You can load data per a schedule you customize for your business operations, and the timing can vary.
Overall, this ETL method for data analysis will ensure you get the most out of your data in a scalable way, so you can have more visibility and make smarter decisions for your business.
