![]() Data extraction drives business intelligence That in turn makes it easy to provide access to data to anyone who needs it for analytics, including executives, managers, and individual business units. How does one deal with changing APIs? What happens when a source or destination changes its format? What if the script has an error that goes unnoticed, leading to decisions being made on bad data? It doesn’t take long for a simple script to become a maintenance headache.Ĭloud-based ETL tools allow users to connect sources and destinations quickly without writing or maintaining code, and without worrying about other pitfalls that can compromise data extraction and loading. The more sources there are, the more likelihood that something will require maintenance. However, when sources are more numerous or complex, this approach does not scale well. This works fine when there is a single, or only a few, data sources. In the past, developers would write their own ETL tools to extract and replicate data. Sign up for free → Contact Sales → ETL: Build-your-own vs. For example, Facebook’s “move fast and break things” approach means the company frequently updates its reporting APIs – and Facebook doesn't always notify API users in advance. Even APIs from reputable, developer-friendly companies sometimes have poor documentation. ![]() APIs are different for every application.While it may be possible to extract data from a database using SQL, the extraction process for SaaS products relies on each platform's application programming interface (API). The load process needs to be specific to the destination. Retrieve the target tables and fields from the records specified by the integration’s replication scheme.Įxtracted data is loaded into a destination that serves as a platform for BI reporting, such as a cloud data warehouse like Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, or Google BigQuery.Changed data structures have to be dealt with programmatically. Check for changes to the structure of the data, including the addition of new tables and columns.Whether the source is a database or a SaaS platform, the data extraction process involves the following steps: Sign up for free → Contact Sales → The data extraction process Because full extraction involves high data transfer volumes, which can put a load on the network, it’s not the best option if you can avoid it. The first time you replicate any source you have to do a full extraction, and some data sources have no way to identify data that has been changed, so reloading a whole table may be the only way to get data from that source. One drawback of incremental extraction is that it may not be able to detect deleted records in source data, because there's no way to see a record that's no longer there. During subsequent ETL steps, the data extraction code needs to identify and propagate changes. Some data sources are unable to provide notification that an update has occurred, but they are able to identify which records have been modified and provide an extract of those records. Most databases provide a mechanism for this so that they can support database replication (change data capture or binary logs), and many SaaS applications provide webhooks, which offer conceptually similar functionality. The easiest way to extract data from a source system is to have that system issue a notification when a record has been changed. Data can be extracted in three primary ways: Update notification Types of data extractionĮxtraction jobs may be scheduled, or analysts may extract data on demand as dictated by business needs and analysis goals. An ETL tool can extract data from these sources and load it into a data warehouse where it can be analyzed and mined for insights into brand perception.ĭata extraction does not need to be a painful procedure. It may have data from many sources, including online reviews, social media mentions, and online transactions. Suppose an organization wants to monitor its reputation in the marketplace. The goal of ETL is to prepare data for analysis or business intelligence (BI). What is Data Extraction? ĭata extraction is the process of obtaining data from a database or SaaS platform so that it can be replicated to a destination - such as a data warehouse - designed to support online analytical processing (OLAP).ĭata extraction is the first step in a data ingestion process called ETL - extract, transform, and load.
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