![]() Since ELT tools use a data pipeline that transfers data from the source to the data store with no steps in-between, organizations cannot address data quality issues before the information reaches the data lake.Ī lack of data cleansing results in more work for data science teams and anyone else that depends on this resource to do their job. Without a way to transform this information, such as through a data masking process, ELT tools limit how organizations can work with the data they collect.ĭata quality affects a variety of business aspects, from the customer information used by front-line workers to the decisions made by leadership. However, if organizations completely drop sensitive data from the extraction process, they may lose out on valuable insights. A lack of compliance may result in fines and other penalties being levied against the company, and these costs can be high. Since ELT technology typically extracts all available data with little to no pre-filtering, sensitive data may mix in with other data types.īy extracting this sensitive data without transforming it, organizations could fall out of compliance with data privacy regulations. This info may live in enterprise resource planning tools, customer relationship management platforms, and other data sources throughout the organization. It includes names, addresses, social security numbers, medical information, credit card numbers, and other personal information. When it comes to maintaining data privacy, ELT falls short in a number of ways. ELT is Bad at Data PrivacyĪs consumers are becoming more aware of data collection and usage, data privacy is becoming a critical issue for many organizations. Here are three big reasons ELT is a bad idea: 1. The ELT world might sound enticing, but it has a dark side that causes many problems for organizations. They also don’t need to assign resources to filter through the data before extracting it, and even raw data can flow into the data lake. As the amount of data companies generate increases exponentially year over year, ELT solutions help organizations consolidate data into a centralized store for their data science teams. The biggest draws of ELT are that organizations don’t need to be selective about the data they extract and they can load it into the data lake quickly. In addition, as new data types develop, data managers can store the information even if they’re not able to use it with their current tools. Given the massive data volumes moving into the data lake, data teams can pick the sets they want to work with their business intelligence tools. Transform: Organizations only transform the data that is being moved at the time of their choosing. Typically, this process is semi- or fully automated, and data loads to the data lake as it collects in the sources. Massive volumes of data move along this pipeline, as the lack of restrictions on what it can store opens up big data functionality. Since the data is untransformed before it loads into the data store, organizations cannot use data warehouses unless they are only working with structured data. Load: Following the extraction, ELT tools take the data and move it to the organization’s data lake. ![]() The ELT tool connects with each data source and extracts the relevant information directly. Business intelligence tools cannot get a full picture of the organization’s data with this configuration. Many organizations have data spread throughout databases, cloud solutions, applications, and many other places. This characteristic enables ELT to work with raw, unstructured, and semi-structured data of all types. How Does ELT Work?ĮLT solutions use a three-step process for the data’s journey from the source to the data store.Įxtract: Because ELT data is moving into a data lake, it doesn’t need to go through any changes before doing so. Once the data moves into the data lake, its transformation occurs on an ad hoc basis. Organizations adopt this type of solution as part of a data pipeline that supports their analytics and business intelligence by moving data into a data lake. ![]() ELT technology is a process that handles large-scale data extraction, loading, and transformation. ![]()
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