Data Extraction: How ETL tools do it?
Data extraction is the method of obtaining data from any data source for replication to a destination such as a data lake or a data warehouse which support online analytical processing (OLAP). Data extraction precedes the data ingestion process using ETL (extract, transform, and load) tools. These tools help to prepare data for analysis or business intelligence.
Businesses often want to maintain their brand image using the data from different sources such as social media mentions, online reviews and transactions. ETL tools can extract data from these sources and load it into a destination where it can be analyzed for deeper insights into brand perception.
Types Of Data Extraction
Data Extraction jobs can be scheduled by data analysts or on-demand based on business needs and applications. Data can be extracted in three different ways:
Data extraction can easily be done from any data source; only the system needs to issue a notification when a record is altered. Most databases have a mechanism for data modification and database replication. Several SaaS applications also provide webhooks, which offer similar features.
Few data sources are unable to provide notification when the data is modified, but they can identify records which have been updated and provide data extracts. During the following ETL steps, the data extraction code needs to classify and deliver updates. One limitation of incremental extraction is that it is unable to identify deleted records in source data as there is no means to do it.
Full extraction is the process that one has to follow during his first data extraction. Few data sources do not have a system to identify modified records so reloading a whole database remains to be the only method to get source data. Full extraction involves high data transfer volumes and puts a higher load on the network; hence this process is not recommended.
How is The Data Extraction Process Performed?
The data extraction process from any data source, such as a SaaS platform or a database involves the following steps:
- Identify the modified structure of the data, which can be the addition of new columns, tables. Updated data structures should be handled programmatically.
- Fetch the required fields and tables from the source data specified by the data replication scheme.
- Retrieve the correct data.
Data extracted from any source is loaded into a destination that supports data analysis and BI reporting. Popular cloud data warehouses are Microsoft Azure SQL, Amazon Redshift, Snowflake and Google BigQuery.
Data Extraction Challenges
Data extraction from a database can be performed using SQL. But, in the case of SaaS platforms, each platform’s application programming interface (API) is considered:
- Different application has different APIs.
- Several APIs, even from popular data sources, are poorly documented.
- APIs are dynamic in nature: changing with time.
Build-your-own Or Cloud ETL tools?
Traditionally, companies rely on developers to design their ETL tools for data extraction and replication. This approach used to be ideal when there were few data sources. With the increase in the number of complex data sources, this method does not scale well. More data sources require more maintenance.
What happens when the format of a source or destination changes? How to deal with varying APIs? What if an error script leads to wrong decisions based on bad data?
Simple scripts can also become challenging to maintain. Cloud-based ETL tools solve this problem of maintenance by allowing users to connect sources and destinations quickly without writing code. Hence, it becomes easy for data analysts, engineers and scientists to access data.
Data Extraction Drives Business Intelligence
You need to clearly understand the context of the data replication from data sources to destinations, and use the right tool to profit from data analytics and BI programs. For popular data sources, there’s no need to build a data extraction tool.
Daton is an automated data pipeline which extracts data from multiple sources to data lakes or cloud data warehouses where employees can use it for business intelligence and data analytics. The best part is that Daton is easy to set up without the need for any coding experience and it is the cheapest data pipeline available in the market.