Extract, transform, load

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The process of ETL plays a key role in data integration strategies. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.

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Overview

A typical ETL process collects and refines different types of data, then delivers the data to a data warehouse such as Redshift, Azure, or BigQuery.

ETL also makes it possible to migrate data between a variety of sources, destinations, và analysis tools. As a result, the ETL process plays a critical role in producing business intelligence & executing broader data management strategies.

How ETL works

Three steps make up the ETL process và enable data lớn be integrated from source to lớn destination. These are data extraction, data transformation, and data loading.

Step 1: Extraction

Few businesses rely on a single data type or system. Most manage data from a variety of sources and use a number of data analysis tools khổng lồ produce business intelligence. Khổng lồ make a complex data strategy lượt thích this work, the data must be able lớn travel freely between systems and apps.

Before data can be moved khổng lồ a new destination, it must first be extracted from its source. In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. Raw data can be extracted from a wide range of sources, including:

Existing databases and legacy systemsSales and marketing applicationsMobile devices and appsCRM systemsData storage platformsData warehousesAnalytics tools

Although it can be done manually, hand-coded data extraction can be time-intensive & prone to lớn errors. ETL tools automate the extraction process and create a more efficient và reliable workflow.


Step 2: Transformation

During this phase of the ETL process, rules & regulations can be applied that ensure data chất lượng and accessibility. You can also apply rules to lớn help your company meet reporting requirements. The process of data transformation is comprised of several sub-processes:

Cleansing — inconsistencies & missing values in the data are resolved. Standardization — formatting rule are applied to lớn the data set.Deduplication — redundant data is excluded or discarded.Verification — unusable data is removed and anomolies are flagged.Sorting — data is organized according lớn type.Other tasks — any additional/optional rules can be applied to improve data quality.

Transformation is generally considered to be the most important part of the ETL process. Data transformation improves data integrity và helps ensure that data arrives at its new destination fully compatible & ready lớn use.

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Step 3: Loading

The final step in the ETL process is to lớn load the newly transformed data into a new destination. Data can be loaded all at once (full load) or at scheduled intervals (incremental load).

Full loading — In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, chất lượng records in the data warehouse. Though there may be times this is useful for research purposes, full loading produces data sets that grow exponentially và can quickly become difficult to maintain.

Incremental loading — A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new & unique information is found. This architecture allows smaller, less expensive data warehouses khổng lồ maintain & manage business intelligence.

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ETL and business intelligence

Data strategies are more complex than they’ve ever been, and companies have access to more data from more sources than ever before. ETL makes it possible to lớn transform vast quantities of data into actionable business intelligence.

Consider the amount of data available to a manufacturer. In addition khổng lồ the data generated by sensors in the facility & the machines on an assembly line, the company also collects marketing, sales, logistics, & financial data.

All of that data must be extracted, transformed, and loaded into a new destination for analysis. In this scenario, ETL helps create business intelligence by:

Delivering a single point-of-view

Managing multiple data sets demands time & coordination, & can result in inefficiencies & delays. ETL combines databases và various forms of data into a single, unified view. This makes it easier khổng lồ analyze, visualize, & make sense of large data sets.

Providing historical context

ETL allows an enterprise khổng lồ combine legacy data with data collected from new platforms & applications. This produces a long-term view of data, so that older data sets can be viewed alongside more recent information.

Improving efficiency & productivity

ETL Software automates the process of hand-coded data migration. As a result, developers and their teams can spend more time on innovation, and less time managing the painstaking task of writing code to move & format data.

Building your ETL strategy

ETL can be accomplished in one of two ways. In some cases, businesses may task their developers with building their own ETL. However, this process can be time-intensive, prone khổng lồ delays, và expensive.

Most companies today rely on an ETL tool as part of their data integration process. ETL tools are known for their speed, reliability, và cost-effectiveness, as well as their compatibility with broader data management strategies. ETL tools also incorporate a broad range of data chất lượng and data governance features.

When evaluating an ETL tool, you’ll want khổng lồ consider the number and variety of connectors you’ll need, as well as its portability và ease of use. You’ll also need khổng lồ determine if an open-source tool is right for your business, since these typically provide more flexibility and help users avoid vendor lock-in.

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