A successful data management and BI strategy must answer some key questions, such as:
- Where does the required information exist?
- How can data from disparate systems be integrated to create a unified view?
- How to transform ETL big data into a format that can be easily analyzed to extract actionable insights?
Extract, Transform, Load or ETL is a technology commonly employed to find answers to these questions and create a single version of the truth. Although primarily a type of data integration, ETL development facilitates data migration, data warehousing, and data profiling.
What is ETL?
As the name suggests, ETL data integration is a three-step process in which data is extracted from one or more data sources, converted into the required state, and loaded into a database or cloud data warehouse.
Step 1: Extract
Businesses collect large amounts of data from a variety of internal and external sources. This data is kept within various databases, and therefore, requires an ETL engine that processes data to create an integrated and complete view of all information assets. The process of ETL for big data starts with identifying data that is significant in supporting organizational decision-making. Once the data sources have been identified, connections are built to the required ETL databases to extract data for transformation.
Step 2: Transform
Perform ETL transformations to bring uniformity to the disparate data definitions of information collected from different data sources. A set of business rules (such as aggregation, joins, sort, union functions) are used to transform data into a consistent format for reporting and data analysis and ensure data consistency across the organization. Transformations are a key part of the ETL process, and the right tool is needed to perform this smoothly to gain valuable insight from the source information.
Step 3: Load
Loading transformed data into a data warehouse, database, data mart, or any other data repository is the last step of managing the ETL job or process. Depending on the volume of data, target database, and the BI needs of the business, any of the following two types of loading methods can be used:
- Full Load – Full Load refers to the initial data load performed to bring data into the data repository for the first time. Since this usually involves transferring large volumes of data, it is important to optimize the process using various techniques, such as parallel processing, load balancing, pushdown optimization, bulk data loading, concurrent workflow execution, and more.
- Incremental Load – Incremental Load is used to synchronize new or updated data between the source database and the target data repository. Using incremental load, enterprises can keep the data warehouse updated with the most recent transactional data available while saving the computing resources and time required to perform a full load every time new data is added to the source systems.
Why Is ETL Important for Data Integration?
Since its introduction, ETL engine development has become a ubiquitous process in the world of data processing and management. From preparing large and disparate datasets for business intelligence and analytics to handling complex data integration scenarios, the use of ETL technology is broadening beyond simple data movements. Hence, it is of utmost importance to have an ETL engine that can perform the ETL process easily on these complex integration scenarios?
Here are a few use-cases where enterprises commonly employ ETL engines:
ETL and Data Migration
Data migration is defined as the process where data is transferred between databases, data formats, or enterprise applications. There are various reasons why an organization may decide to migrate data to a new environment, such as replacing legacy applications with modern ETL platforms, switching to high-end servers, or consolidating data post-merger or acquisition.
Regardless of the underlying reason, ETL technology remains a proven method that many organizations rely on to respond to data migration needs. Using code-free ETL software tools, businesses can surface data from different data repositories and consolidate data from external and internal sources to offer business users a unified, well-rounded view of all business operations.
ETL and Data Warehousing
Data warehousing is a complex process as it involves integrating, rearranging, and consolidating massive volumes of data captured within disparate systems to provide a unified source of BI and insights. In addition, data warehouses must be updated regularly to fuel BI processes with fresh data and insights. Performing ETL is a key process used to load disparate enterprise data together in a homogenized format to a data repository. Besides, with incremental loads, real-time BI ETL tools also enable almost immediate data warehousing, thereby providing business users and decision-makers fresh data for reporting and analysis. BI ETL tools are needed for visualizations for a better understanding of the insights from the ETL process.
ETL and Data Quality
From erroneous data received from online forms to lack of integration between data sources and the ambiguous nature of data itself, there are several factors that impact the quality of incoming data streams, thereby diminishing the value businesses can extract from their data assets. Hence, applying data quality rules during the ETL data integration process will increase accuracy in data.
ETL is a key data management process that helps enterprises ensure that only clean and consistent data makes it to their data warehouse and BI tools. Here are some of the ways businesses can use ETL architecture to enhance data quality:
- Data profiling and standardization
- Data consolidation
- Data enhancement
- Data cleansing and verification
ETL and Application Integration
For a better view of enterprise information assets, integrating data stored in disparate applications such as Salesforce.com and MS Dynamics is mission-critical. End-to-end ETL development tools help integrate data from applications, massage the data when performing the ETL process for ensuring data quality and load it into a target destination such as a data warehouse or ETL database.
Why Do Businesses Need ETL Tools?
It is crucial for a business to use a structured, formatted data format before loading it into the required storage system. The process of ETL, i.e., extract, transform, and load, offers significant functions that can optimize business’ capabilities:
- Provides a holistic view of data where current data can be viewed alongside the old, historical data.
- Improves decision-making and business intelligence (BI) which consequently leads to higher revenue and increased cost-savings.
- ETL platforms make it easier to analyze, visualize and understand big data sets.
- Increases productivity by codifying and automating the process of ETL. This means employees can spend their time on other tasks instead of wasting it on repetitive tasks.
How to Select the Right Enterprise ETL Tools?
There are different enterprise ETL development tools or solutions available. Although a fairly simple process to understand, ETL data integration processes can grow in complexity as the volume, variety, and veracity of data being transformed increases. Generally speaking, the following factors can impact the scope and complexity of performing an ETL process and need to be considered when opting for the right ETL platform out of different enterprise ETL tools:
- The number and variety of data sources and destinations involved.
- The number of tables created.
- The type of transformations required. This may range from simple look-ups to more complex transformation data flows, such as flattening the hierarchy of an XML, JSON, or COBOL file or normalizing data.
To successfully address these challenges, use different types of ETL products, and create a comprehensive, accurate view of enterprise data. Businesses need high-performance code-free ETL software tools that offer native connectivity to all the required data sources. These ETL processes and tools should be capable of handling structured, semi-structured, and unstructured data and built-in job scheduling and workflow automation feature to save the developer resources and time spent on managing data.
Here is a round-up of the features businesses should look for in enterprise-ready, high-performance code-free enterprise ETL tools:
- Library of Connectors – Well-built top ETL tools should offer native connectivity to a range of structured and unstructured, modern and legacy, and on-premise and cloud data sources. This is important because one of the core ETL jobs of different ETL software is to enable the bi-directional movement of data between the wide variety of internal and external data sources that an enterprise utilizes.
- Ease of Use – Managing custom-coded ETL mappings is a complex process that requires deep development expertise. To save developer resources and transfer data from the hands of developers to business users, you need an enterprise ETL solution that offers an intuitive, code-free environment to extract, transform, and load data.
- Data Transformations – The data transformation needs of a business may vary from simple transformation jobs such as lookups and joins to more complex tasks like denormalizing data or converting unstructured data into structured tables. Therefore, to cater to these data manipulation needs, you should select top-performing ETL processes and tools that offer a range of simple and more advanced transformations.
- Data Quality and Profiling – You only want clean and accurate data to be loaded into your data repository. To ensure this, look for an ETL platform that offers data quality and profiling capabilities to determine the enterprise data’s consistency, accuracy, and completeness.
- Automation – Large enterprises handle hundreds of ETL jobs daily. The more of these tasks you can automate, the faster and easier it will be for you to extract insights from data. Therefore, look for an advanced ETL automation solution with job scheduling and process orchestration, and automation capabilities with a powerful ETL engine.
While these are a few important features top ETL tools must-have, the right selection of ETL software tools will depend on the volume, variety, velocity, and veracity of data your enterprise handles.
Improve ETL Performance with Enterprise ETL Tools
Long-running data transformation and load jobs that run for hours are not unusual for ETL data integration administrators. As data volumes and disparity grow, ETL processes and dataflows may become more complex, taking up more computing resources and developers’ time.
Here are a few ways you can optimize the performance of your ETL jobs in software:
Data management solutions with a parallel processing ETL engine support fast processing of large data files by splitting them into small chunks. Each chunk can then be processed separately in parallel, ensuring optimal utilization of computing resources and accelerating the data pipeline.
Pushdown Optimization (ELT)
Pushdown optimization or Extract, Load, Transform (ELT) is a variation of ETL that involves pushing down the transformation logic from the staging area to the source or target ETL database. This is done to avoid unnecessary movement of data and speeds up ETL performance.
Incremental Data Load
Incremental data load, a technique that involves loading only the changed data to the destination, helps save time and computing resources by eliminating the need to perform full data loads every time data needs to be refreshed in the data repository. More about Change Data Capture (CDC) or incremental data loads can be found here.
Streamline ETL Processes with Enterprise ETL Tools
Astera Centerprise is an enterprise-level ETL software that integrates data across multiple systems, such as SQL Server, Excel, Salesforce, and more. It enables users to manipulate large data sets using a comprehensive set of built-in transformations. It helps move the transformed data to a unified repository for advanced ETL flows, all in a completely code-free, drag-and-drop manner.
ETL application in software utilizes a high-performance cluster-based architecture, industrial-strength ETL flow engine, and advanced automation capabilities to simplify and streamline complex ETL processes. With support for pushdown optimization, incremental data load, and connectivity to legacy and modern data sources, Astera Centerprise helps businesses integrate data of any format, size, or complexity with minimal IT support in a code-free ETL environment.
Interested in giving Astera Centerprise, one of the top enterprise ETL tools, a try? Download your free 14-day trial or watch this demo video for a quick walkthrough of an enterprise-level simple ETL tool and data integration platform.