Data Integration Demystified: Picking the Perfect Techniques

People in creative professions often tend to mystify their work. They argue that nothing will come of it without contact with the muse and how to contact it — everyone has their secret knowledge. Data engineers are creative people. But their craft is available to everyone, although it requires many special skills. They also apply to the correct choice of data integration techniques.

Business Results for Data Integration Leaders

Harmonizing Diverse Data into a Unified Format

You have data stored in different systems, databases, spreadsheets, and from external sources (APIs). Each might have its format, structure, and language for storing data. Data integration techniques are the strategies you employ to gather, transform, and merge this diverse data so that it is understood and utilized effectively. DATAFOREST will do the same, you just need to arrange a call.

ETL As a Conveyor Belt

ETL gathers raw materials (data), processes and refines them (transform), and then delivers the finished products (cleaned and structured data) to the storage area (target destination). This streamlined process ensures that data is ready to be used effectively, which is typical for data integration techniques.

Extraction

This phase calls for gathering data from various source systems: databases, files, APIs, spreadsheets, or other data storage mediums.

• Determine which sources contain the data you need. It could be sales data from a CRM system, customer information from a database, or financial data from spreadsheets.
• Extract data from the source systems. It means running queries on databases, pulling data from APIs, reading files, or connecting to external systems. The extracted data might be raw and unstructured.
• Analyze the extracted data to understand its structure, quality, and potential issues. This step helps identify data that needs cleaning or transformation.

It’s a crucial part of data integration techniques.

Transformation

In the transformation phase, the extracted data is made suitable for analysis and reporting:

• Remove or correct inconsistencies, errors, and duplicate records. It ensures data accuracy and reliability.
• Convert data into a consistent format. For example, you might standardize date formats, convert currencies, or unify measurement units.
• Enhance data by adding additional information. It requires looking up missing data, geocoding addresses, or appending demographic information.
• Summarize or aggregate data to a higher level of granularity. For example, you might calculate total sales per region or average customer age.
• Combine data from different sources to create a unified view. It implies merging customer data from various databases or systems.

Transformation is used in many data integration techniques.

Loading

The loading phase entails placing the transformed data into a target destination — a data warehouse or a database:

• First, the transformed data is staged in a temporary storage area. It helps ensure the data is ready and validated before loading into the target destination.
• Map the transformed data to fit the schema of the target destination. It ensures that the data aligns with the format of the destination database or data warehouse.
• Load the transformed data into the target destination. It concerns inserting records into database tables or populating data into specific sections of a data warehouse.
• Perform validation checks to ensure that the data was loaded correctly. It means comparing source and destination record counts or verifying against predefined rules.
• Depending on the target system, you might create indexes or optimize the data for faster querying and analysis.

It’s the course of techniques for data integration.

ETL: Crafting a Clean Canvas

Centralizing data and performing essential transformations before storage in data integration methods take disparate data and transform it into a valuable asset for analysis, insights, and strategic planning.

Centralization of Data

One of the primary benefits of ETL is its ability to centralize data from various sources into a single repository, often a data warehouse or a centralized database. It offers several advantages:

• Centralized data is easily accessible by various departments and teams, promoting a single source of truth for decision-making.
• By pulling data from different systems into a central location, ETL reduces the creation of data silos that hinder collaboration and analysis.
• Centralized data provides a comprehensive view of the operations, enabling more profound insights and cross-functional analysis.

Data centralization is a necessary attribute of many data integration techniques.

Data Transformation for Consistency

Data extracted from different sources might be in varying formats and structures. ETL tackles this issue by transforming the data into a consistent form before storage:

• ETL processes ensure data adheres to a standard format, making it easier to analyze and compare across different sources.
• Any inconsistencies, errors, or inaccuracies are addressed during the transformation phase, improving data quality.
• Data is transformed to align with the schema of the target destination, ensuring uniformity and ease of querying.

Data transformation in the context of data integration techniques refers to converting, modifying, and restructuring data from source format into a suitable for the target system.

Improved Data Quality

ETL's data transformation and cleaning steps significantly enhance data quality:

• Duplicate records are identified and eliminated, preventing redundant and inaccurate information.
• Consistent data is corrected, minimizing the risk of making decisions based on flawed information.

Improved data quality in data integration techniques enhances data accuracy, completeness, and reliability.

Better Analysis and Reporting

By centralizing and transforming data, ETL lays the groundwork for practical analysis:

• Clean data is ready for analysis, reducing the time spent on data preparation.
• Improved data quality ensures that reports and insights are accurate and trustworthy.

Improved data quality in different types of data integration methods enhances consistency, completeness, and reliability.

Streamlined Decision-Making

ETL contributes to informed decision-making by providing a reliable foundation:

• ETL processes are designed to work near-real-time, enabling up-to-date insights for time-sensitive decisions.
• Decision-makers can access a comprehensive view of the data using strategic choices based on a holistic understanding.

In data integration methodology, streamlined decision-making is the ability to access, process, and analyze integrated data efficiently.

ELT: Change One Letter

ELT is associated with cloud-based data warehouses that handle complex transformations on the stored data without requiring a separate transformation step before loading. It's a strategy that uses modern data storage, processing technologies, and data integration techniques.

Transformations After Loading

ELT follows a similar ETL pattern but with a slight rearrangement of steps. Data is extracted from source systems, loaded directly into the target destination, and transformed within the target system itself. So, ETL focuses on preparing the data before it's loaded into the target destination, while ELT focuses on performing transformations after the data is loaded. The last option has the following advantages:

1. Modern data warehouses and cloud-based systems are optimized for large-scale data processing. ELT takes advantage of this scalability by performing transformations within these systems.
2. ELT allows for near-real-time or real-time transformations on the data as it arrives, enabling up-to-the-minute insights.
3. ELT eliminates the need for a separate transformation step, simplifying the overall data integration architecture.
4. With data loaded as-is, analysts and data scientists have direct access to the raw data for exploratory analysis, even before transformations are applied.
5. ELT accommodates changing business requirements by allowing transformations to be adjusted within the target system.

In the context of ELT data integration techniques, apply data transformations and modifications directly within the target data warehouse or repository.

When to Choose ELT

Choose ELT if you're working with a modern, robust data warehouse or a system that handles complex transformations efficiently. ELT is well-suited for scenarios where data must be processed in real-time or near-real-time. Here are methods where ELT is more suitable.

1. Modern cloud-based data warehouses, like Amazon Redshift, Google BigQuery, and Snowflake, are designed to handle large-scale data processing. ELT aligns well with these systems, leveraging their processing power for efficient transformations.
2. ELT is a better choice if your team requires real-time or near-real-time insights. With ELT, data is loaded as it arrives, and transformations are performed on the fly, enabling you to access fresh insights without delays.
3. ELT allows analysts and data scientists to access raw data directly in the data warehouse. This flexibility is ideal for exploratory analysis and ad-hoc queries, as they can work with the data before applying transformations.
4. ELT simplifies the data integration architecture by eliminating the need for a separate transformation stage. It reduces complexity, streamline processes, and make maintenance easier.
5. ELT is advantageous if your data requires intricate transformations that are better executed using the advanced processing capabilities of modern data warehouses.
6. Modern data warehouses are designed to scale horizontally and vertically. ELT uses this scalability to ensure data transformations are efficiently distributed across clusters for faster processing.
7. Some modern data warehouses offer cost-efficient pricing models where you pay for the processing capacity you use. ELT's ability to leverage this processing capacity effectively contributes to cost savings.

Choose ELT, among other data integration techniques, when you have a robust target data warehouse with powerful processing capabilities. Interested in the update? Schedule a call and we'll tell you what's what.

Data Virtualization

It is a data integration technique that allows users and applications to access and query data from multiple sources as if it were all in a single location. Instead of physically moving or copying data into a centralized repository, Data Virtualization provides a virtual layer that aggregates and presents data from various sources in real-time, on-demand, and without data duplication.

How It Works

In methods of data integration, virtualization creates a virtual layer that enables real-time querying of data from various sources without physically moving or duplicating the data.

• Data virtualization solutions create a virtualization layer between the data sources and the users or applications. It is often managed by specialized software.
• The virtualization layer maintains a metadata catalog describing the data's structure, location, and characteristics in various source systems.
• When a user or application submits a query, the data virtualization platform optimizes it based on the metadata and routes it to the appropriate source systems.
• The data is accessed in real-time from the source systems, and the results are combined and presented to the user or app as if they were coming from a single source.

The meaning of virtualization work in data integration techniques is creating a simulated or virtual environment that abstracts and isolates underlying resources.

Advantages of Data Virtualization

The advantages of data virtualization were formed in data integration techniques through its ability to provide real-time data access.

• One of the key benefits of data virtualization is that it eliminates the need to move or replicate data physically. The data remains in its original location, reducing the complexity and resources required for replication.
• Users access data in real time, enabling timely decision-making and analysis.
• Data virtualization provides a unified view of data from multiple sources, making it easier for users to work with data without worrying about its origin.
• Data virtualization simplifies data integration by abstracting the underlying complexity of different data sources.
• Since data isn't duplicated or stored in a central repository, teams save on storage costs and reduce the maintenance overhead.
• New data sources can be integrated quickly, making data virtualization a flexible solution in dynamic business environments.

In data integration techniques, data virtualization reduces data replication, offers cost efficiency, enhances agility, and simplifies data integration.

Universal Intermediary — ESB

An Enterprise Service Bus (ESB) is a centralized infrastructure that facilitates communication, integration, and interaction between various company applications, systems, and services. In data integration techniques, it is a middleware that enables different software components to communicate and exchange data seamlessly, regardless of their underlying technologies or protocols.

• When one application wants to communicate with another, it sends a message to the ESB. The ESB is responsible for routing the message to the appropriate destination. This routing might involve sending the message to one or more other apps or services.
• Applications use different data formats and protocols for communication. The ESB transforms the data from one form to another if needed. Data integration techniques ensure that the message is converted into a format that the receiving application can understand.
• Different applications might communicate using protocols like HTTP, SOAP, REST, or JMS. The ESB can perform protocol conversions, enabling applications to communicate seamlessly despite differences in communication protocols.
• The ESB often employs message queuing mechanisms. When an application sends a message to the ESB, the ESB can place the message in a queue. The receiving application then retrieves and processes the message from the line at its own pace.
• The ESB handles error logging if any issues occur during the communication process, such as message delivery failures. In data integration techniques, it logs errors, triggers alerts, and even attempts to retry sending the message.
• The ESB implements security measures such as authentication and encryption to ensure that communication between applications is secure and that only authorized parties access the data.
• In more complex scenarios, the ESB also orchestrates the execution of multiple services to achieve specific business processes. It coordinates the interactions between different services to achieve a particular outcome.

The role of an Enterprise Service Bus (ESB) in data integration techniques is to serve as a centralized middleware that facilitates communication, integration, and interaction between different apps.

Identifying Changes with Data Integration Techniques

Change Data Capture (CDC) is a technique used in data integration to capture changes made to a database or data source in near real-time, allowing them to be tracked and replicated in a separate system for analysis or synchronization purposes. It helps keep target systems updated with the latest changes without repeating the entire dataset.

CDC Sequencing

The Change Data Capture sequencing is formed as a data integration method by capturing and tracking changes in source data using mechanisms like virtual database logs.

1. CDC systems continuously monitor the source data, such as a database, for any changes. 
2. The CDC system identifies the specific record or data that has been modified.
3. Once the change is identified, the CDC system captures the details of the change.
4. CDC often relies on log or journal mechanisms present in many database systems.
5. After capturing the changes, the CDC system propagates this info to target systems.
6. The captured changes are applied in the same sequence they occurred in the source. 
7. Since the CDC captures changes, the target systems are updated in near real-time.

The pleasure of Change Data Capture sequencing lies in its ability to provide accurate and chronological tracking of data changes due to data integration techniques.

Beneficial Use Cases

CDC is beneficial when keeping data synchronized and up-to-date is crucial.

Data Warehousing

CDC is extremely valuable in data warehousing scenarios where organizations must populate their data warehouses with the latest information from source systems. Instead of constantly replicating entire datasets, the CDC captures only the changes, ensuring that the data warehouse remains up-to-date and aligned with operational procedures to data integration techniques.

Real-Time Reporting

CDC ensures that the reporting databases are continuously updated with the latest changes for teams that require real-time or near real-time reporting in data integration techniques. It allows business analysts to generate reports based on the current data, enabling timely insights for decision-making.

Data Synchronization

CDC is ideal for scenarios where data must be synchronized between different systems. When a client updates information in a customer relationship management (CRM) system, CDC propagates those changes to other systems like billing, marketing, and support due to data integration techniques.

Business Intelligence

CDC is a crucial enabler for business intelligence initiatives with data integration techniques. By feeding data changes into data marts and analytical systems, CDC ensures that analytical models and reports are based on the latest information, enhancing the accuracy and relevance of insights.

E-commerce and Online Transactions

In e-commerce, online transactions, and finance, timely information is critical. Like many data integration techniques, CDC helps update order statuses, inventory levels, and customer data across various systems, ensuring accurate transaction processing and customer experiences.

The State of Enterprise Data Integration, 2020

Right Data Integration Techniques

As one of the leading data engineering providers, DATAFOREST is guided by client requirements, data volume, complexity, existing infrastructure, scalability, and business impact when choosing data integration techniques.

We also consider vital factors such as budget, security, flexibility, and business goals. If you want to discuss your case during reading, please fill out the form, and let's get in touch!

FAQ

What is the primary purpose of the Extract, Transform, Load (ETL) technique?

The primary purpose of the Extract, Transform, Load (ETL) technique is to extract data from various sources, transform it into a usable format, and load it into a target destination for analysis and reporting like other data integration techniques.

When should I consider using the Extract, Load, Transform (ELT) approach over ETL?

Consider using the Extract, Load, Transform (ELT) approach over ETL when you have a robust target system for data integration techniques, such as a modern data warehouse, capable of efficiently handling complex transformations on the loaded data.

How does data virtualization differ from traditional data integration techniques like ETL and ELT?

Data virtualization differs from traditional data integration techniques like ETL and ELT by providing a virtual layer that allows real-time access to data from multiple sources without physically moving or replicating it, enabling seamless querying and analysis.

Can you provide an example of implementing Change Data Capture (CDC)?

Change Data Capture (CDC) in action could involve capturing and replicating real-time updates made to customer records in a CRM system to a data warehouse for sales analysis in order of data integration techniques.

Are there any hybrid approaches that combine different data integration techniques?

Yes, hybrid approaches can combine techniques like ETL and data virtualization to leverage both strengths for comprehensive data integration.

What factors should I consider when selecting my organization's proper data integration technique?

When selecting from data integration techniques for your organization, consider factors such as data volume, latency requirements, data complexity, existing infrastructure, scalability, budget, security, and alignment with business goals.

How do modern trends like AI/ML impact the effectiveness of these data integration techniques?

Modern trends like AI/ML impact the effectiveness of data integration techniques by requiring more agile and real-time integration to feed data-hungry AI/ML models. It emphasizes techniques like ELT, CDC, and data virtualization to provide timely and accurate data for analysis and predictions.

How do enterprise data consolidation and federation collectively contribute to maintaining data integrity?

Enterprise data consolidation gathers and centralizes data from various sources, while data federation enables a unified view of distributed data sources, collectively ensuring data integrity by maintaining accuracy and consistency. This seamless data propagation across systems enhances overall data management, providing a holistic approach to effectively handling and utilizing enterprise data due to data integration methodologies.