Data Integration is the process of integrating or collaborating data from multiple sources or systems like flat files, multi-dimensional databases, and data cubes, into a single source or a target system to satisfy the needs of an organization. A target source is usually a data warehouse or a data lake.

Data refers to a collection of facts and statistics gathered across various sources like the company, internal operations, Websites, Apps, social media sites, surveys, cookies, etc. This data is then stored in databases to be used as and when required. However, with the exponential increase in the amount of data being collected today, there is a need to have a systematic and standardized process to integrate this data. This is where Data Integration comes into the picture. 

According to Statista, the total volume of data was 64.2 zettabytes in 2020; it is predicted to reach 181 zettabytes by 2025. This abundance of data can be overwhelming if you aren’t sure where to start. The data to be integrated may not always be in the same format as they have different parent sources. Therefore it is important to first convert them into a standardized, compatible, and common format that matches the data format of the target system.

To make sure this is taken care of, data integration is performed through an ETL

ETL: EXTRACT TRANSFORM LOAD

ETL is a process where we extract the data from various sources, transform them to match the required data formats, filter it to meet the requirements of the organization, and then load it into the respective target system.

EXTRACTION
The data extracted is from different sources and can be unstructured, structured, or in hybrid form. There are three extraction methods that can be used.

1. Partial Extraction (with update notification): When the source system is able to inform about when the records have been changed.
2. Partial Extraction (without update notification): When the source system is unable to inform about when the changes happened but can inform which records have been changed.
3. Full Extraction:  When the source system is unable to tell when or which records have been changed.

TRANSFORMATION
Data transformation can be a challenging and crucial part of the process as it ensures data consistency, quality, and integrity which is vital for any database.
It ensures the cleansing and mapping of the data to a specific schema, mainly of the target system.
This data gets stored in a staging database before loading it to the final target system.

LOAD
It is the final stage of ETL in which the transformed data gets loaded from the staging database into the target system or Data warehouse.

TYPES OF TOOLS USED FOR ETL

1. CODE-BASED TOOLS: If the integration is performed by programmers and developers, you can use code-based tools. These tools provide a certain degree of flexibility and create a more complex ETL. Code-based tools are better for troubleshooting and are easier to manage. e.g. python coded, airflow, bonabeau, etc.

• UI-BASED TOOLS: These tools provide a Graphical User Interface (GUI) for ETL tasks. You can select from the many options that are displayed to you and complete the task.
  These types of tools are preferable when the ETL is performed by analysts or non-developers who have little to no knowledge of coding. e.g. Ab Initio, Xplenty, Talend, etc.

• HYBRID TOOLS (UI/ CODE BASED): Some of these tools are a combination of both coding and UI. e.g. SSIS SQL Server, IBM Datastage, Informatica, etc.

Also Read: The next generation Data Fabric platform

WHY DO WE NEED DATA INTEGRATION?

Every organization aims to provide its customers with the best facilities and increase its public engagement. Some of the ways in which Data Integration helps organizations perform this task efficiently are:

• Collecting customer data from all sources available gives the organization all details they require about the needs and interests of its customers. This data may include contact details, website visits, social media activities, survey details, etc. While improving the marketing efforts enhances customer service as well.
• Analyzing an organization’s revenue, budget, productivity, performance, and operations for better decision-making.
  This upgrades the overall management and improves the strategic design of future possibilities. This process is aided by visual reports, charts, graphs, and maps.
• Boosts efficiency of data analysis process as the required data will be available in one place.
• Reduces time and effort to collect data from multiple sources every time data analysis is to be performed.
• As the data is filtered and cleansed during the transformation process, it is of a more valuable and precise form. 

DATA INTEGRATION ISSUES

• Integrating data of varying schemas: While integrating data from various sources, the data stored may follow different schemas in their respective databases. Integrating them without proper conversion or formatting will result in incorrect and inconsistent data in the target system. e.g. If database A consists of customer data in the format ( cutomer_id, customer_name, customer_contact) and database B consists of data in the format (customer_no, customer_name). If you integrate data from these sources, both the fields (customer_id and customer_no) though having the same type of data will be stored separately as it is difficult for an automated system to identify their similarity.
  
• Data Redundancy: Sometimes databases consist of unwanted attributes. These attributes need not be stored in the integrated system and so we have to make sure that such attributes are not integrated automatically. e.g In an employee database (emp_id, emp_name, date of birth, age), the age can be calculated using current data and date of birth, therefore age can be redundant and unwanted data and shouldn’t be integrated into the target system.

• Data value conflicts: Inconsistency in data values may arise if those values are under similar fields but expressed in different formats. e.g While integrating revenue generated by an organization in multiple countries, the data under the revenue field in India will be expressed in Rupees whereas in the U.S it will be expressed in Dollars, or the data under the date field will be expressed in DD/MM/YYYY format in India whereas in MM/DD/YYYY format in the U.S. Proper conversion of these data, values must be ensured.

• Choosing the best-suited ETL tool: Choosing an ETL tool is an important task. Many of these tools have intermediate functionalities which are not always required by the task being performed. Since we have multiple options to choose from which provide varying operations, we must choose according to what works best for the task at hand.

ScikIQ Connect i.e., ScikIQ Data Integration layer is a NO Code Data Integration and Data Transformation Platform that let our client teams effortlessly centralize all the data & build a single version of truth thereby enabling them to make faster, smarter & confident decisions using data.

Also Read: Data lakes and Data warehouse

Using ScikIQ Connect, client teams can build and deploy Data Integration and Data Transformation Pipelines without writing a single line of Code. The engine takes care of all the complexities in the background thereby saving time and engineering effort – hence creating bandwidth for the client team to work on actual value-added activities and make sense of the data.