Data Integration is defined as the process of combining data from various sources into a unified view. It begins with ingesting raw data and includes steps such as Cleansing, Data Transformation, and ETL mapping. Data Integration allows analytics tools to produce actionable, effective Business Intelligence.
Data Integration solutions generally involve elements like a network of data sources, and clients accessing data from the Master Server. The process consists of the client sending a request to the master server for data. Then, the master server extracts the data needed from external and internal sources. The data is extracted from the sources followed by accumulating it into a single, cohesive dataset. This data is relayed back to the client for further use.
Here are a few common use cases where Data Integration tools are deemed valuable:
- Simplifying Business Intelligence: Since Data Integration provides a unified view of data from numerous sources, it simplifies Business Intelligence (BI) processes of analysis. Enterprises can easily comprehend and visualize the available datasets to derive actionable and valuable information on the current state of business. Data Integration allows analysts to compile more information for more accurate evaluation without being overwhelmed by data volume.
- Leveraging Big Data: Data Lakes can be quite voluminous and highly complex to deal with. Companies like Google and Facebook, for instance, process a continuous influx of data from billions of users. This level of information consumption is described as Big Data. With more and more Big Data enterprises making their way into the market, more data becomes available for businesses to leverage. This means that sophisticated Data Integration efforts are integral to several Business Organizations.
- Creating Data Lakes and Data Warehouses: Data Integration initiatives are primarily used by large businesses to create Data Warehouses, which integrate multiple data sources into a Relational Database. Data Warehouses let users compile reports, run queries, generate analyses, and retrieve data in a consistent format. For instance, various enterprises depend on Data Warehouses like AWS Redshift, Google BigQuery, Snowflake, and Microsoft Azure to generate Business Intelligence from their data.
Types of Data Integration Strategies
Here are the different Data Integration strategies that can be implemented based on the business needs:
- Application-based Integration: This is an approach to integration wherein software applications Retrieve, Locate, and Integrate data. During the integration process, the software should make data from different systems compatible with one another. This allows them to be transmitted from one place to another.
- Common Storage Integration: Common Storage Integration is the most frequently used approach for storage within Data Integration. Duplicate data from the original source is kept in the integrated system and refined for a unified view. This is the underlying principle behind the traditional Data Warehousing solution.
- Uniform Access Integration: Uniform Access Integration focuses on building a front end that makes data appear consistent when accessed from multiple sources. But the data is left within the original source. This method can be deployed by Object-oriented Database Management Systems to create the appearance of uniformity between databases.
- Middleware Data Integration: In this integration approach, a middleware application acts as a mediator. This normalizes the data and brings it into the Master Data Pool. Since legacy applications have difficulty working with other applications, middleware comes to the rescue. It can be leveraged when a Data Integration system can’t access data from one or more legacy applications on its own.
- Manual Data Integration: Here, an individual user manually collects the necessary data from multiple sources by accessing interfaces directly, followed by cleansing and combining it into one Data Warehouse for future use. This can be highly inconsistent and inefficient for larger enterprises. This Data Integration process might be a viable solution for small organizations with minimal data resources.
Best Practices for the Data Integration Process
Here are the best practices for the Data Integration process:
- Determine Data Sources to Include: You need to decide which data sources to include based on the business case at hand. IBM and other Traditional Mainframe Systems continue to play an integral role in the operations of most large businesses and many small and medium-scale companies. These systems have core transaction data that is integral to most Data Integration initiatives. Companies should also take a look at their disparate software systems and identify the role that data from each of those systems would play in meeting the objectives laid out in the business case.
- Begin with an End in Mind: For optimal performance, the Data Integration process should begin by establishing a clear objective for the project. This is because well-executed Data Integration projects can yield tangible results.
- Determine Data Communication Methods: There are various things to consider when determining how data should be communicated. It is vital to consider both current and future volumes of data to track whether Data Pipeline capacity will be adequate to handle the traffic.
Introduction to Data Migration
Data Migration is defined as the process of moving data from one system to another that involves a change in the database or application and storage. There are various use cases of Data Migration. They might need to establish a new Data Warehouse, upgrade databases, merge new data from an acquisition or other source, or overhaul an entire system. You would also have to leverage Data Migration when deploying another system that works next to existing applications.
Types of Data Migration Strategies
Here are the different types of Data Migration strategies that can be used based on different business needs:
- Trickle Migration: The Trickle Data Migration strategy completes the Data Migration process in phases. During implementation, the new and old systems run in parallel. This eliminates any operational interruptions or downtime. Processes that run in real-time can keep data continuously migrating. The implementation can be quite tricky, but if done right, it helps reduce risks.
- Big Bang Migration: In this process, the full transfer is completed within a limited window of time. Live systems experience downtime while the data undergoes ETL processing and transitions onto the new database. It is limited by the fact that it all happens in a one-time boxed event. Therefore, it requires relatively little time to complete. Since the business operates with one of its resources offline, the pressure can be intense and risks a compromised implementation.
Best Practices for the Data Migration Process
Here are the best practices for the Data Migration process:
- Sticking to the Strategy: Often, Data Managers make a plan and then abandon it when the process goes too smoothly or when things go kaput. The Data Migration process can be frustrating and complicated at times, so you need to prepare for that reality and then stick to the plan.
- Backing Up the Data Before Executing: If something goes wrong during the implementation, you can’t afford to lose data. Therefore, you need to make sure that there are backup resources and that they’ve been tested before you move forward.
- Extensive Testing: During the design and planning phases, and throughout maintenance and implementation, you need to test the Data Migration to make sure you will eventually achieve your desired outcome.
Data Integration vs Data Migration
There are various differences between Data Integration and Data Migration. Firstly, Data Integration from external sources is a prerequisite for Data Analytics. This is because organizations are trying to provide a 360-degree view to their customers. On the other hand, Data Migration is a process that is carried out when storage mediums or new systems come into play. It is also leveraged when enterprises need to move their existing resources to a different environment. Data Migration is a one-time process when implementing a new application whereas Data Integration is a continuous process that keeps the business running on a daily basis.
Conclusion
This blog talks about the differences between Data Integration and Data Migration. It also gives a brief overview of Data Integration and Data Migration that includes its benefits, use cases, and best practices before diving into the differences between the two.
If you’re looking for a Data Migration tool to suit your business requirements, Hevo might be the answer. A fully managed No-code Data Pipeline platform like Hevo Data helps you integrate data from 100+ data sources (including 30+ Free Data Sources) to a destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.