Data mapping is a primary step in a wide range of data management processes, such as data conversion, integration, warehousing virtualization, etc. It converts data from the source format to target-compatible format, establishing connection between two distinct datasets to accomplish a range of transformation and integration jobs. The complexity of data mapping tasks varies depending on the structure of source and destination systems, and the data being mapped.
Using data mapping, enterprises can collect information from diverse sources and transform it to get actionable insights.
Data extraction is the process of retrieving data from structured, semi-structured, or unstructured sources, such as emails, PDFs, text files, etc. It enables enterprises to use data for further processing, so it can be aggregated, analyzed, migrated to a central repository, or used for reporting.
Extraction is the first step in the ETL process, following which the data is cleansed, transformed, loaded into the relevant destination system.
The process of modifying the structure or format of source data to make it compatible with the destination system is called data transformation. It is used in various data management processes, including data integration, migration, cleansing, replication, etc.
Transforming data offers users several benefits, such as:
- It makes data better organized, making it readable for both computers and humans.
- Properly structured and formatted data improves data quality, and ensures accurate results when integrated or analyzed.
- Transformed data ensures that applications can communicate with each other despite the difference in the storage format of source and destination systems.
ETL is the abbreviation for extract, transform and load. An ETL process:
- Retrieves data from a source system, such as file, database, etc. – Extraction
- Changes into a format which is compatible with the destination – Transformation
- Stores it into a targeted database or data warehouse – Loading
Pushdown optimization, also known as ELT, is a server load-balancing technique that maximizes the performance of integration processes. It extracts, loads, and transforms data – enabling users to choose whether the data processing takes place in the source or target database.
By placing the staging table in the database, it eliminates the unnecessary data movement and reduces network latency, cutting down the overall execution time.
Pushdown optimization modes can be categorized into two types:
1- Partial Pushdown: In this mode, the transformation logic is partially pushed down to the source or destination database, depending on the database provider.
2- Full Pushdown: It pushes down transformation logic completely to the database, executing the job in pushdown mode from beginning to end.
ETL (extract, transform and load) extracts data from multiple sources, transforms the data from one format to another, and then loads it into the target database or data warehouse.
ELT (extract, load and transform), on the other hand, extracts data from a source, loads it into a target database, and transforms data within that database. However, for ELT to work, source and destination systems should both be databases.
The major difference between these two processing techniques is where the transformation takes place.
- In ELT, the integration server handles the load of transformation, whereas in ELT, the transformation takes place in the source or destination database.
The process of combining data from heterogeneous sources and presenting it in a unified format is known as data integration. This includes:
- Consolidating data from a wide variety of source systems with disparate formats, such as file systems, APIs, databases, etc.
- Cleaning data by removing duplicates, errors, etc.
- categorizing data based on business rules
- Transforming it into the required format so it can be used for reporting or analysis
Data integration is used in various data management processes like data migration, application integration, master data management, and more.
Data migration is the procedure of moving data between disparate systems, including databases and files. Nevertheless, ‘transfer’ is not the only step in migration. For instance:
- If the data is in different formats, the migration process includes mappings and transformations between the source and target systems.
- It also involves evaluating quality of the source data before loading it into the destination system.
The efficiency of any data migration project depends on the diversity, volume, and quality of data being moved.
Data validation is the method of removing invalid values, duplicates, and other errors to ensure the accuracy and quality of data prior to processing. the process makes certain that the data is:
- Comprehensive and consistent
- Unique and free of errors
- Compliant with business requirements
Validating data is essential for all data processes, including integration, migration, warehousing, etc. as the end goal is to help ensure the accuracy of the results. Working with reliable data gives businesses the confidence to take timely decisions without hesitation.
Data cleansing, also called data scrubbing, is a primary step in the data preparation process. It comprises of finding and correcting errors, duplications, format issues, and other inaccuracies in a dataset to ensure the quality of data. The need for data cleansing increases when the data is coming from disparate sources, with varying formats and structures, as it has to be standardized for analysis and reporting.
Data quality evaluates the accuracy and reliability of data based on custom business rules. It includes a set of attributes that ensures high-quality data is used in decision-making, reporting, and other business processes.
Some critical dimensions of data quality include the following:
- Completeness ensures that no information is lost or missing from any data set.
- Consistency indicates that data across different systems is synchronized and shows similar information.
- Accuracy ensures whether the data is correctly showing what it should. It can be assessed against the source data and authenticated via user-defined business rules.
- Uniqueness guarantees that the information is free of duplications.
- Validity ascertains that the data complies with the criteria and standards set by the business user.
Data profiling is used to evaluate the data by presenting a full breakdown of its statistical characteristics, such as error count, duplication ratio, warning count, minimum and maximum value, and more. It facilitates a detailed inspection by assisting users in recognizing risks, quality issues, and overall trends of data.
Data profiling is used in a range of data management processes, including:
1- Data migration
2- Data integration
3- Data warehousing
4- Data synchronization
Change Data Capture (CDC) facilitates real-time data integration by capturing individual changes made in the source data and propagating them to the destination system. The process is mainly used for data synchronization. Since it replicates data is in near real-time and only deals with the data changes, it makes for a scalable, and time-and-cost effective option.
Database integration combines information from multiple sources, including databases, cloud, files, and more, and stores it in a unified database for a clean, consolidated view.
Storing information in a centralized database ensures enterprise-wide availability of data to stakeholders and partners. Moreover, it improves user experience and reduces information delivery time.
API integration enables applications to connect with backend enterprise systems through APIs. APIs include a set of protocols, routines, or tools that help applications interact with each other, as well as databases and devices.
Using an API integration platform, enterprises can create and add new APIs into the enterprise eco-system to:
- Connect to cloud applications
- Extract value from legacy data sources
- Automate integration processes
Data consolidation is the process of collecting and integrating data from disparate sources into a unified system, such as a data warehouse or database. The process can be implemented using different techniques, such as data integration, warehousing, or virtualization.
Data consolidation offers various benefits, such as:
- Consolidating enterprise data provides users a 360-degree view of their business assets.
- It allows companies to plan and implement business processes, and disaster recovery solutions based on this information.
- It speeds up process execution and simplifies information access.