A well-designed data warehouse empowers users to perform queries that deliver high-quality results to support faster decision making across the enterprise. Among the key benefits of building a data warehouse (or a subset data mart) is to store variant data while extracting business value and maintaining historical data for record keeping. As a centralized depository, a data warehouse provides a stable, integrated, time-variant collection of enterprise data derived from various sources that can be used for organizing, analyzing, and classifying crucial information from all business areas according to subject. Enterprises have relied on the extract, transform and load (ETL) process for decades to get a consolidated view of data relevant to their initiatives to make better business decisions.
As a large-data software tool, ETL can be used for data cleansing, profiling, and auditing to validate quality and ensure all information is trustworthy. Designing and creating the extraction process is often one of the most time-consuming but also important tasks of ETL. Since extracting data correctly sets the stage for the success of subsequent processes, it starts with identifying the data sources including all rows, columns, and fields to be extracted from various systems. Applying a basic workflow, raw data from different locations is copied or exported in different formats and stored in a staging area for further processing.
An important function of transformation is data cleansing, which aims to pass only correct data to the target. An intrinsic part of the ETL process involves data validation to confirm whether the data extracted from different sources has the expected values. The challenge occurs when different systems have to interact in the “transform data” stage where a series of rules or functions are applied to the extracted data to prepare it for loading into a data repository. Any rejected data is identified for further analysis to rectify, archive, or purge as incorrect or bad records. ETL is best suited to flatten or de-normalize data to fit the needs of the targeted data warehouse.
During the load phase of ETL the extracted and transformed data is loaded into the end target, which can be any data store including a simple delimited flat file, data mart, or data warehouse. Depending on the needs of the enterprise, this process varies widely, and the timing and scope to replace or append data are strategic choices that are dependent on the business requirements. Whereas ETL tools were traditionally designed for developers and IT staff, Mycelium ETL software provides powerful capabilities for non-technical business users so they can create connections and perform data integrations as needed, rather than burdening the organization’s IT staff.
Limitless Possibilities Using Mycelium ETL Manager
By applying the software’s three-major functions, the Mycelium ETL Manager has the capability to retrieve data from multiple sources (extract), perform data conversions and implementations (transform), and populate data warehouses and data marts (load). For businesses wanting to harness the potential offered by big data, Mycelium’s dual integration techniques deliver an exponential number of data formats, systems, and sources along with other powerful features, such as:
- Improve Data Quality – Mycelium software handles Big Data and ETL Manager enables advanced cleansing and data profiling before data is loaded to a new repository like a data warehouse or data mart.
- Transform Data into Desired Format – ETL Manager provides a standardized process to aggregate and transform data using multiple integration techniques for an exponential number of data formats, systems, and sources.
- Analyze Critical Data – ETL Manager applies rules to detect any unintended changes to data over its entire life-cycle as the result of a storage, retrieval or processing operation, including malicious intent and human error.
- Purge or Archive Stale Data – Stale data in production environments can decrease overall performance and increase maintenance costs. ETL Manager helps archive or remove stale and unused records.
- Execute Data Conversion Activities –ETL Manager can extract data from legacy systems, transform it, and load it into your current CIS system. EDMS preserves the data relationships while generating keys and summary data.
ETL Manager provides the ideal framework to extract data from different sources, transform it, and then load it in the appropriate data warehouse or data mart. Moreover, it empowers non-technical team members with zero knowledge to write scripts with excellent results. Well-managed data warehouses can merge large quantities of critical information for a more holistic analysis using a complete dataset that enables data-driven decision making based on streamlined data conversions from all areas of the enterprise.
In today’s data-first business environments, the consumption of extracted data unleashes limitless possibilities for organizations looking to gain an advantage. At Mycelium Software, our ETL Manager delivers a streamlined approach to large-scale data conversions. Please contact firstname.lastname@example.org, or call 1(904) 473-4959 for more information about our ETL Manager designed for team members with zero technical skills.