Data Migration and Integration

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As organizations grow and become more complex, data integration becomes increasingly important. By collecting, storing, and analyzing data from a wide variety of source systems, you can have a unified view of your entire organization, gain actionable insights, and make informed decisions with accurate data. However, the data integration process can be highly challenging and could require significant amounts of time and expertise.

There are several methods that organizations can use to bring their data together depending on their requirements. In this blog post, we will discuss the different types of data integration that organizations can use to combine and transform their data. Each data integration method has its advantages and drawbacks, so it is crucial to understand how each one works to choose the right type for your organization.

What is Data?

Data is a broad term that includes all recorded information that has been formatted in a certain way to be used in the future. This information is collected, stored, processed, and analyzed for specific purposes.

What is Data
What is Data

Organizations collect data from a wide variety of sources, such as ERP and CRM systems, legacy systems, transaction records, market research, products or services, customer data, social media, and much more. This data is a vital resource for organizations across different industries, as it provides insights for decision-making, process optimization, and innovation.

Data can take many forms and can be classified as either quantitative, which refers to measures of counts or values expressed in numbers (numeric values), and qualitative, which is categorical, descriptive, and conceptual, such as photographs or observations.

We can also divide data into structured, semi-structured, and unstructured categories. Structured data is information organized and formatted typically in a database or spreadsheet and is easily searchable and queryable, such as data tables in a database or rows and columns in an excel spreadsheet. Semi-structured refers to data that is not as strictly organized as structured data but has some underlying structure and can include elements such as tags or metadata, which provide some context and organization to the data. Some examples include XML files, JSON data, and emails. Unstructured data does not have a pre-defined structure or format. This type of data is often unorganized and complicated to search and query. Some examples of unstructured data include text documents and video files.

What is Data Integration?

Typically, data is siloed in disparate systems, data sources, and formats, making it difficult to access, combine, and analyze. Data integration refers to the process of uniting the data and transforming it into accessible and meaningful information, which provides useful and actionable insights. In other words, data integration means combining data from disparate sources to create a unified view, making data more valuable than it previously was. Additionally, the process involves filtering useless data and loading filtered data into an interface generally used by data analysts, business intelligence professionals, or other users who need access to the data. The integrated data is easier to use, analyze, and interpret, by having the complete picture of the organization.

Types of Data Migration and Integration

Why should you integrate all your data?

Integrating data across your organization has many benefits, such as eliminating data silos, increased efficiency and productivity, enhanced customer experiences and services, and improved data integrity and security. Data integration can also improve your ROI by eliminating the need to manually transfer the data from data sources into a storage location, automating many data integration processes, such as identifying issues and removing useless information, enhancing customer satisfaction, and improving the availability of processed information for making decisions. It will allow organizations to make decisions with a whole picture of the organization, create personalized experiences for their clients, innovate and develop better products, improve operations and customer service, and much more.

Why Should You Integrate Data


ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration approaches that are used for moving data from one place to another, so it can be transformed into a format that is suitable for analysis, reporting, and decision-making. The main difference between ETL and ELT is the order in which data transformation and loading into the target system is performed.

ETL is a data integration approach that involves extracting data from its source, transforming it to fit the target data store’s requirements, and loading it into the target data store. It is best used for data that needs to be transformed before being loaded into the target data store. Organizations use ETL tools to extract and filter data from their sources, transform data into a meaningful format, and finally transfer data to the desired destination, which could be a database or data warehouse.

ETL Data Consolidation
ELT Data Transformation

On the other hand, ELT is a newer data integration approach that involves extracting data from its source and loading it into the destination without transforming it beforehand. This technique is best used for data that doesn’t need to be transformed before being loaded in a centralized location.

Both methods are useful for consolidating data from multiple sources and transforming it into a format that is suitable for analysis and making decisions. ETL and ELT processes can be implemented either manually or automatically by using the right data integration tools.

Data Integration Tools

Data integration has become a critical function in organizations across many industries. However, as mentioned above, data integration processes are challenging, and without an adequate data integration tool, they become more complicated and time-consuming.

Data integration tools are software solutions that are used to extract data from multiple data sources, transform the data, and load it into a destination system. These tools can be used to support a wide range of business processes, such as business intelligence, customer relationship management, supply chain management, and financial reporting. Organizations should carefully evaluate the objective, resources, and tools, to have an efficient and successful integration process across all data sources.

Types of Data Integration

In this section we will discuss the characteristics of 05 different types of data integration:

Data Consolidation

Data consolidation is the process of combining data from multiple sources into a single location or integrated view. This can be done in a variety of ways and for different reasons, such as to clean the data, improve data accuracy and integrity, reduce data redundancy, or make it easier to analyze and report on data and improve decision-making.

Consolidating data may involve physically bringing all data together, for example, by extracting data from distributed sources and then storing it in a centralized location, such as a data warehouse. The idea is to move data to a unified destination; in other words, to create one data store for all the information, bringing down the number of storage locations.

Data Consolidation

In other cases, data consolidation can be used to create a virtual view of the data without physically moving it, a method known as data virtualization. Data warehousing and virtualization are important concepts in the data management field and should be considered when developing a data integration strategy.

Overall, data consolidation is an integral part of data integration. Through this process, organizations can improve the quality and integrity of their data. Data consolidation also provides an overall view of the organization that can be interpreted to gain actionable insights.

Data Consolidation
Data Warehouse

Data Warehouse

Data warehouses are used for large-scale, big data integration processes that involve consolidating data from multiple sources into one central repository, in other words, storing data in a data warehouse where it is monitored and maintained. The data is usually structured so it can be easily queried and analyzed.

Data warehouses are recommended for data that needs to be accessed and analyzed on a regular basis, such as customer or sales data, and for data that needs to be aggregated, sorted, and filtered before being loaded into the target data storage system, such as Amazon Redshift, Google BigQuery, and Snowflake.

Data warehousing can either be done manually using scripts or automatically using easy-to-use and cost-effective data integration solutions such as KingswaySoft. By taking advantage of these data warehousing integration tools, organizations can ensure data sources are properly integrated before being loaded into the destination.

Data Virtualization

Data virtualization involves unifying data from multiple sources into one virtual view, through a single interface, without actually copying the data. By creating a virtual data layer on top of existing data stores, the data can then be queried and analyzed using tools such as Microsoft SQL Server Analysis Services (SSAS).

This method is useful when data from various sources need to be combined for analysis without the need to permanently store it in a single data store. Organizations can take advantage of data virtualization and access data from multiple sources in real time without having to store it in one central repository. It can be used to create a virtual data warehouse or data lake that is built on top of multiple sources.

A subset of data virtualization, called data federation, can also be a powerful method to integrate data and create a single, logical view of data from multiple sources. Data federation typically involves the creation of a layer that maps all data from different sources into a common format that allows users to query the data as if it were all stored in one location, as a virtual database.

Data Virtualization
Data Propagation

Data Propagation

Data Propagation involves replicating data from one data source to another and is used when data needs to be replicated across multiple sources or when data needs to be in sync for consistency and accuracy. This powerful type of data integration ensures data synchronization and consistency. Additionally, this approach is considered event-driven and can be synchronously or asynchronously, which we will discuss below.

Data propagation can be done manually using scripts, or automatically using data integration tools that copy data from a location to the desired destination. KingswaySoft supports many of these software technologies, such as SQL Server, PostgreSQL, Azure Synapse, Oracle Database, IBM DB2, and MySQL.

There are numerous types of data propagation, which include:

  • One-way data propagation: the aim is to transfer data from one location to another in a single direction without any return communication.

  • Two-way data propagation: in this case, it involves transmitting data in both directions between two locations, allowing for bidirectional communication.

  • Multicast data propagation: this process involves transmitting data from one source to multiple destinations simultaneously.

  • Broadcast data propagation: refers to transmitting data from a single source to all possible destinations within a network.

  • Point-to-point data propagation: this method involves transmitting data directly from one location to another without passing through intermediate nodes.

  • Hub-and-spoke data propagation: the transmission of data from a central location, or the hub, to multiple locations, referred as the spokes.

  • Mesh data propagation: This process involves transmitting data between multiple locations in a network and allowing multiple paths for data transmission.

Data Propagation

Data Replication

Unlike data consolidation, which aims to create a single, comprehensive dataset, data replication involves copying data from its source and storing it in a separate data store, in other words, copying data from one location to another. This data can then be used for backups, archiving, or creating data marts, which are typically designed to make an organization's data available for specific departments or teams.

Data replication is an important part of data management, as it helps to ensure the availability and integrity of data. It is commonly used in distributed systems, such as cloud computing environments, where data is stored and accessed across multiple locations. This process can be achieved manually or automatically using tools such as Apache Kafka, MySQL, MongoDB, PostgreSQL, and Hadoop Distributed File System (HDFS), all of which are supported by KingswaySoft's SSIS Productivity Pack. This process is best suitable for data that doesn’t require to be transformed before being stored in the destination.

Data Replication

Data Integration Strategies

Now we will discuss the main data integration techniques that organizations can use to combine their data and eliminate data silos. These data integration methods are typically divided into five main categories:

Manual Data Integration

Manual data integration is a term that refers to replicating and merging data from different sources manually, typically by a data engineer, through custom coding, programming, and scripting, without using any specialized software or tools. This manual integration approach is the most basic and often used when there are few sources or when data must be manipulated in complex ways and can't be done with other data integration techniques.

By using a manual data integration technique, data can be integrated across multiple data sources efficiently and securely. However, this can be time-consuming and requires a certain level of expertise by a data engineer to set up and use effectively.

Middleware Data Integration

Middleware data integration is a strategy that involves using middleware software to connect multiple sources and achieve data consolidation, replication, and virtualization. This method works well for translating and automating bi-directional communication between modern and legacy systems, or when data needs to be published in real-time. It can also be used to integrate data from different platforms or environments, such as on-premises systems and cloud-based systems.

By leveraging middleware data integration tools, such as IBM MQ and Apache Kafka, data can be easily, efficiently, and securely integrated across multiple sources.

Application-Based Integration

This term refers to using software applications for replicating and integrating data from multiple source systems. This integration technique is frequently used to streamline the exchange of data between different systems or applications, or to consolidate data from various sources for analysis and reporting.

There are several ways that application-based integration can be implemented, such as:

  • Application Programming Interfaces (APIs): facilitate data integration by allowing data systems to communicate, interact, and exchange information using pre-defined protocols. APIs enable a standardized and secure way for data systems to interact with each other without needing complex programming or scripts. However, they are not ideal for integration tasks involving large volumes of data because the process can take longer than ETL or ELT processes.

Application-Based Integration
  • APIs are useful for real-time data transfer and integration if they are designed to support fast, low-latency communication. KingswaySoft supports REST, OData, and SOAP API technologies and protocols/architectural guidelines.

  • ETL tools: These are specialized software applications that are used to extract data from multiple sources, transform it into a usable form, and load it into the destination system. ETL tools work well for data consolidation purposes.

  • Data integration platforms: This term refers to comprehensive software solutions designed to facilitate data integration from multiple sources. These platforms typically include a range of tools and features for ETL development, as well as support for diverse data formats and protocols.

In summary, the application-based integration approach is an effective way to replicate, consolidate, and exchange data between different systems and applications. It can help organizations improve their efficiency and decision-making by creating a unified view of data from numerous sources through data consolidation and virtualization.

Application-Based Integration
Uniform Data Access Integration (UDAI)

Uniform Data Access Integration (UDAI)

Uniform data access is a term that refers to the ability to access data from different sources consistently, using a uniform data model to create a single view of the data. This data integration strategy works well for data needs to be accessed and queried from multiple sources in a consistent and standardized way.

Organizations can use the uniform data access approach to ensure regular access to data, regardless of the data source. For example, it can be used to integrate data from different departments within an organization. Data access integration tools, such as ADO.NET, ODBC, and OLE DB, can be employed to facilitate data access and query performance.

Uniform access integration can be implemented through data federation or data virtualization, which, as discussed above, provide a unified interface for querying and accessing data from multiple sources. By providing a consistent and uniform interface for accessing data, it reduces the complexity and effort involved in working with data from diverse sources, making it easier for users to access and analyze data from multiple sources consistently.

Common Storage Integration

Common storage integration refers to a data integration system that facilitates the merging of data from multiple sources. It works well for uniformly presenting data from different platforms and environments, such as on-prem and cloud systems, creating and storing copies, and performing the most sophisticated data analysis tasks with consolidated data. The information undergoes data transformation before copying it into a data warehouse, such as Amazon Redshift, Google BigQuery, and Snowflake, so your systems only have to access one data source.

Using a common data storage integration strategy can be more expensive than other data integration methods that integrate systems without a central repository, such as APIs or middleware, because of the larger storage and maintenance that is required. However, this higher cost can be offset by the benefits this system provides, which include improved data quality, security, and consistency, reduced data duplication, and enhanced data accessibility. Furthermore, it can help improve efficiency and productivity within an organization, leading to cost savings and higher profits over time.

Common Storage Integration


Developing a data integration strategy is an important process for any organization that wants to take full advantage of its data. There are different data integration methods for bringing data together, each with its own advantages and drawbacks.

Organizations should carefully evaluate their data requirements before deciding which data integration approach and tools best fit their needs and ensuring that their data is integrated in the most efficient way possible. By using a proper strategy, organizations can access the data they need when they need it and get the most out of it, ensuring data governance, data quality, data integrity, better customer experiences, and potentially increased profits.

Choosing the Right Data Integration Tool

KingswaySoft provides powerful and sophisticated SQL-server-based data integration solutions specifically designed to handle the most complex and demanding integration challenges. With these software solutions, organizations of all sizes can easily and efficiently integrate their data from multiple systems, including databases, a cloud data warehouse, file servers, and more. Additionally, leverage a wide array of SSIS components with advanced capabilities such as data transformation, data cleansing, encryption, automation, value mapping, big data integration, and much more, making it easy to transform and normalize data as it is being integrated.

KingswaySoft provides robust, flexible, and cost-effective data integration solutions so you can take control of your data. Click here to find out more.

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About KingswaySoft

KingswaySoft is a leading integration solution provider that offers sophisticated software solutions that make data integration simple and affordable. We have an extreme passion for our software quality and an intense commitment to our client's success. Our development process has always been customer-focused, we have been working very closely with our customers to deliver what benefits them the most. We have also made sure that our support services are always highly responsive so that our customers receive maximum benefit from the use of our products.

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