RightData’s Complete Data Integration Guide

November 22, 2022
RightData’s Complete Data Integration Guide

No matter your industry, it's critical to use a secure, efficient and scalable system to handle your data. If your organization still uses a legacy system, you may be one of the many businesses that are being held back from modernization and streamlined processes. As a leader or primary decision-maker in your company, you likely juggle many responsibilities, but a legacy system can make managing complex data seem even more time-consuming and disconnected.

New data integration methods, frameworks, and strategies can now ease the burden of growing complexity and volume and help you and your company benefit from tools that can bridge the gap between your manual operations and automation. In this data integration guide, we'll discuss various data integration steps, examples, tools and what to look for in your modern solutions.

What Is Data Integration?

Data integration is the process of moving data between internal and external databases, targets and sources. These databases include data warehouses, production databases and third-party solutions which generate and integrate various types of data. This approach seeks to combine data and integrate it from separate sources to make it easier for users to view data from one unified view. This one view is often called the single source of truth or “SSOT.”

By consolidating this data into a usable dataset, users get more consistent access to various types of critical information. This allows organizations to meet the information requirements of all applications and business processes.

Because fresh data enters your organization every second, it must always be accessible for analysis and made ready with data wrangling techniquesThis is where data integration steps in and makes your data meaningful and useful. Data integration is one of the primary components of the overall data management process, helping users share existing information and make better-informed decisions. Data integration also plays a key role in the data pipeline, which encompasses:

  • Data ingestion
  • Data processing
  • Transformation
  • Storage

Today, as we move toward modern data systems, we can also use the power of the Delta Lake framework, where raw data moves to data transformations and onto useable data in a “medallion” approach of bronze, silver and platinum layers. Data integration is very much a part of that process as well.  This guide outlines the ETL process; very much relevant in data management today.

With the help of the right tools and platforms, data integration enables effective analytics solutions to help users receive actionable, accurate data that drives business intelligence reports and learning. That being said, there is no one-size-fits-all approach to data integration. Though many data integration techniques tend to have common elements, such as a master server and a network of data sources, organizations can use these tools in many different ways to meet their specific needs.

For some, a typical enterprise data integration process involves a user or company requesting data from the master server. Then, the master server will intake the requested data from internal and external databases and other sources. Finally, the data is extracted and consolidated into a cohesive data set for the client to view and use.

It's no secret that technology has evolved rapidly in the last decade, bringing about a global digital transformation that millions of people rely on every day. Today, data comes in many different formats, structures and volumes, and it's more distributed than ever. Data integration is the framework or architecture that seeks to enhance visibility and allow users to see the flow of information through their organization.

As an organization that still relies on a legacy system, your company may not only be at a significant disadvantage compared to competitors, but you can also miss out on many potential benefits, such as:

  • Unlocking a layer of connectivity to help you stay competitive
  • Achieving data continuity and seamless knowledge transfer
  • Increasing operational efficiency by reducing manual processes
  • Promoting intersystem cooperation
  • Accessing better data quality
  • Generating more valuable insight through data that can be easily analyzed

While your organization may not have a problem collecting large amounts of data, properly integrating it can be a challenging task.

Types of Integration Platforms

Your organization can collect, process, manage, connect and store data in many ways. There are various data integration types that make it easy for companies with unique needs and requirements to find the right process. Here's an overview of different integration platforms organizations use today.

1. Integration Platform as a Service (IPaaS)

For easier management and real-time integration application, many organizations rely on Integration Platform as a Service. This platform moves data directly between applications and combines it with various processes and systems to create an accessible user interface. IPaaS enables disjointed applications, systems and databases to communicate with each other no matter where they are hosted for faster project delivery.

This type of platform has become more popular in recent years due to its scalability, flexibility and multi-functional capabilities. Research shows that 66% of organizations plan to invest in IPaaS services to address automation and data integration challenges. . In fact, PaaS is helping companies integrate applications and build workflows without any coding or scripting.

2. Customer Data Platform (CDP)

A customer data platform is a data integration platform that collects and moves data between cloud apps and other sources and sends it to various destinations. CDPs enable marketing and growth teams to gain insight into behavior and user trends and sync these insights with third-party tools to help organizations deliver customized experiences and improved marketing campaigns.

This means that organizations don't have to rely on data or engineering teams. Using a central hub and predefined data models, CDPs facilitate modern transformation capabilities by combining all touchpoint and interaction data with a company's product or service.

3. Extract, Transform and Load (ETL)

Using a robust, built-in transformation layer, the Extract, Transform and Load platform migrates raw data from different cloud applications and third-party sources to data warehouses to undergo a transformation. Once the data is extracted, it must be validated. Next, the data is updated to meet the organization's needs and the data storage solution's requirements.

This transformation can involve standardizing, cleansing, mapping and augmenting the data. Finally, the data is delivered and secured to offer accessible information to internal and external users.

4. Extract, Load and Transform (ELT)

Similar to an ETL platform, an Extract, Load and Transform integration platform performs the load function before the transform function. This platform exports or copies data from many different source locations, but rather than migrating it to a transformation area, it loads the data to the target location, often in Cloud, where it will become transformed.

The biggest difference between these two platforms is ELT does not transform any data in transit, while ETL transforms it before it's loaded into the warehouse. The target source for ELT can also be a data lake, which holds structured and unstructured data at a large scale.

5. Reverse ETL

In a reverse ETL platform, data moves from a warehouse to various business applications, such as marketing, analytics or customer relationship management software. When the data is extracted from the warehouse, it's transformed to meet specific data formatting requirements of the third-party solution. Finally, the data is loaded into the system where users can take action.

The term, “reverse ETL,” refers to the fact that data warehouses don’t load data straight into a third-party solution. Data must be transformed, and since this transformation is performed inside the data warehouse, it's not a traditional ETL. Organizations may use this type of platform for extracting data on a regular basis and loading it into marketing, analytics and sales tools.

How Does Data Integration Work?

One of the most significant benefits of data integration platforms is they can also empower organizations with something called, “data observability.” Users can benefit from data observability by using it to facilitate data integration. In your data integration platform, you should be able to access the following activities for observability and visibility:

  • Monitoring: Enable users to view an operational and holistic view of the entire system or data pipeline through a unified dashboard.
  • Alerting: Data integration platforms should provide alerts so organizations can track expected and unexpected events.
  • Tracking: Visibility through data integration allows users to set and monitor specific events that relate to their unique needs.
  • Comparisons: The data integration platform should be able to monitor events over time and make comparisons to look for anomalies
  • Analysis: Users should have access to automated issue detection that adjusts the specific pipelines and organizational data health.
  • Logging: Record all events in an organized, standardized format to help users reach a faster resolution.
  • Service-level agreement (SLA): Measure pipeline metadata and data quality against your organization's predefined standards in the SLA.

While some organizations may already engage in a few of these activities, the differences lie in how they connect to your end-to-end data operations workflow and how much context they provide on specific data issues. For example, observability is siloed in many organizations, which means the metadata you collect may not connect to other events occurring across teams.

One key point for data observability is that it provides management beyond just monitoring how good or bad your data is across the enterprise. Data observability also encompass a Return on Investment (ROI) aspect that measures the downtime of an enterprise where is there is poor data performance or errors. In fact, the Mean Time To Detect (MTTD) and Mean Time to Reconcile (MTTR) help show the cost of the downtime in hours of bad data.

The Process of Data Integration

To make raw data ready for data integration in data analytics, it must undergo a few critical stages. Consider these data integration process steps.

1. Data Gathering

Also known as data collection, data gathering is the first step in the process of data integration. In this step, data is gathered from many different sources, such as software, manual data entry or sensor feeds. This process allows users to find answers to trends, probabilities and research problems as well as evaluate potential outcomes. Once the data is collected, it will be stored in a database.

2. Data Extraction

In this phase, raw data is extracted from the database, files or other endpoints where the collected data remains so it can be replicated to a destination elsewhere. Once the data is extracted, it will be consolidated, processed and refined into a centralized location, such as a data warehouse. From here, it will await further processing, transformation or cleansing.

3. Data Cleansing

Data cleansing, also called data scrubbing, is the next component of the data integration process. This step involves modeling, fixing and removing corrupt, irrelevant, replicated, incomplete or inaccurate data within a specific dataset.

During the data gathering stage, there may be plenty of opportunities for data to become mislabeled or duplicated, which can produce unreliable outcomes. Cleansing solves this issue.

Though no data cleansing process is the same due to the many differences between each dataset, organizations can set specific templates and needs to ensure data is cleansed and modeled to meet certain business requirements.

Data cleansing and data transformation, though sometimes used interchangeably, are not the same thing. While data cleansing removes data that does not belong, data transformation converts data from one structure or format to another.

4. Data Utilization

Once data reaches the stage of utilization, it is ready for users to view, analyze and use to power products and business intelligence. Data utilization refers to how organizations use data from various sources to improve productivity and operational efficiency in their workflow. This utilization can help organizations facilitate business activities, analyze what actions to take, develop strategies and meet company goals.

Key Characteristics of Data Sources and Destinations

Data integration can be performed in many ways, including manually or with software solutions. However, the manual strategy is often much slower, unscalable and prone to errors. The programmatic approach involves a suite of tools and solutions known as a data stack. This data stack makes it possible for organizations to partially or completely automate the data integration process. Before we dive into the data stack components, understand the underlying data model that resides in every application that reflects how these tools work.

A data model is a specifically structured visual representation of an organization's data elements and their connections. Data models support the development of effective data integration systems by defining and structuring data in relation to relevant business processes.

On the other hand, a database schema defines how the data will be organized within a database. Schemas act as blueprints for transforming a data model into a database. The destination for this data is often a data warehouse, which stores structured data into highly unique formatting rules for machine interpretation, such as using rows and columns.

Components of a Data Stack

Now, let's discuss the makeup of a modern data stack, which can be hosted on the premises or in a cloud application. There are various types of data integration technology within a data stack that are used for programmatic data integration, including:

  • Data sources: Data is collected from various data sources, including applications, databases, files, digital events and many other endpoints.
  • Data pipeline: Data flows through a pipeline made up of connectors that extract raw data from various sources and load it into a specific destination. These data connectors may sometimes transform the data by normalizing or cleaning it to make it readily available for analysts.
  • Destinations: Data will be loaded into either data lakes or data warehouses. These central data repositories permanently store massive amounts of data. While data integration in data warehouses tends to follow a relation structure, data lakes usually accommodate large file storage.
  • Transformation and modeling layer: Data must almost always be transformed to make it ready for analysis. Data transformation may be different every time, but it usually involves reformatting data, performing aggregate calculations, joining tables together or pivoting data. These transformations may take place within the data warehouse as an ELT process or within the data pipeline as an ETL process.
  • Analytics tools: Data reporting makes it easier for organizations to catch issues proactively. These analytics tools within a data stack are used to produce dashboards, summaries, visualizations and other types of reports to help businesses identify how to improve their bottom line.

ETL's Role in Data Integration

When planning your system, note there are two classic main data integration approaches for organizing data stacks. The first approach is ETL, which has developed unique challenges, and the other is ELT, which leverages continuing advancements in technology.

The ETL Workflow

The data integration project workflow for ETL involves the following steps:

  1. Determine the ideal data sources.
  2. Identify the precise analytics needs the project aims to solve.
  3. Scope the schema and data model that end-users and analysts need.
  4. Construct the pipeline that includes extraction, transformation and loading activities.
  5. Analyze the transformed data to extract insights.

In the Extract, Transform and Load workflow, the extraction and transformation functions are tightly connected because they must both be performed before any data can be loaded to a new location. Because every organization has specific analytics needs, every ETL pipeline is entirely custom-built. The ETL workflow must also be repeated under certain conditions, which include:

  • Upstream schemas change: In this condition, data fields may be added, changed or deleted at the source. For instance, a certain application might start gathering new customer data. Upstream schema changes break the specific code used to process the raw data into data models ready for analysis.
  • Downstream analytics change: As analytics require change, they will need new data models. For example, an analyst or end user might want to develop a new attribution model that requires multiple data sources to connect in a unique way.

ETL Data Integration Challenges

The ETL data integration process comes with its fair share of drawbacks. These challenges primarily result from the fact that the extraction and transformation functions precede the loading function, which means transformation stoppages may occur and prevent data from being deposited at the new destination. This can lead to significant data pipeline downtime. Challenges can include:

  • Constant maintenance: The ETL data pipeline performs both data extraction and data transformation. As a result, whenever upstream schemas or downstream data models change, the data pipeline becomes disjointed, requiring the code base to be revised.
  • Customization limitations: Because data pipelines perform advanced transformations that are custom-built to the specific needs of analysts and end users, ETL requires tailor-made codes.
  • Reallocating resources: The ETL system often requires dedicated, skilled engineers to build and continually maintain data integration because of its bespoke code base.

The ELT Workflow

The Extract, Load and Transform workflow enables the ability to store untransformed data in data warehouses to create a new data integration architecture. Since transformation happens at the end of the workflow, this process prevents the upstream schemas and downstream data models from interfering with the extraction and loading functions. This is often what causes failure states in the ETL process.

ELT involves a simpler, shorter and more robust approach. Here's a breakdown of the ELT project cycle:

  1. Determine the ideal data sources.
  2. Conduct automated extraction and loading functions.
  3. Identify the analytics needs the project aims to solve.
  4. Develop data models by creating transformations.
  5. Analyze the transformed data to extract insights.


Though we briefly discussed some differences between ETL and ELT, here's a quick overview of some other distinctions between the two processes:

  • ETL integrates summarized or subsetted data. ELT integrates more raw data.
  • ETL transformation failures stop the pipeline. ELT transformation failures do not stop the pipeline.
  • ETL designs data models and predicts use cases beforehand or fully revises the data pipeline. ELT can create new use cases and develop data models at any time.

How Do You Build Your Modern Data Stack?

Since we already know the components of a data stack, it's important to know what to look for in terms of the features of these components. For instance, you'll want to look for solutions that leverage advancements in third-party tools, automation and cloud-based tech. Here's what to consider and what to look for when building your modern data stack tools:

  • Data pipelines: Organizations will want a tool that comes with built-in connectors to various company data sources. This tool should also have a quick setup, allow for easy scalability and be fully managed to account for schema changes.
  • Destination: The data destination should provide scalability for both storage and computing processes without significant downtime. This efficiency will help accommodate analytics and storage needs. Other destination features should be considered, such as how to run analytics models and provision future role-based access control.
  • Transformations: The transformation tool should make it easier for organizations to trace data lineage, including documentation that helps identify the impact of transformation on your tables and version control. Transformation tools should also be compatible with the destination.
  • Business intelligence (BI) and data visualization tools: Companies should consider technical implementation, BI report testing, user accessibility and visualization flexibility. Some leaders may want to offer self-serve tools for end-users, but these considerations are generally based on the internal data structure.
  • Data integration: There are many different types of data integration tools, but your organization should consider one that has machine learning and AI technology within decision learning as well as the ability to connect to other tools and support continuous delivery.
  • Data warehouse: When it comes to storing data in a warehouse, find a solution that can help you organize, analyze and make the most out of your data. Organizations that use efficient, scalable data warehouses can benefit from insights that help with business growth.

With RightData, your organization can benefit from seamless data integration, reconciliation and validation with a no-code platform. Dextrus is targeted to enhance your organization's data quality, reliability, consistency and completeness. Our solution also allows organizations like yours to accelerate test cycles.

As a result, you can reduce the cost of delivery by enabling continuous integration and continuous deployment. Dextrus allows automation of the internal data audit process and helps improve coverage all around. If you're looking for a modern data stack tool that increases your confidence in being audit-ready, Dextrus could be the one for you.

Learn How to Integrate Your Data With Dextrus

At the end of the day, there are endless possibilities for moving data from one location to another for your organization to analyze and use. If you're still relying on a legacy system, you may be missing out on many integration capabilities and benefits. We know how important it is to make well-informed decisions for the success and future of your organization.

While switching to a new solution to handle your crucial business data can be intimidating, we have the sophisticated tools, knowledge and experience to help you accelerate data integration and transformation and improve your existing data practices. Contact our expert team today or Book a demo to learn more about our platforms and solutions.