Data Integration 101: Modern No-Code Best Practices

Data integration refers to combining data from disparate sources in different formats and structures into a single consistent data store. This single coherent view of data eliminates data silos and allows organizations to understand their data to comprehensively enable analysis, data science models, and decision support.

The earlier versions of “extract, transform, and load” (ETL) tools were designed to extract data from a single source, convert the data into the format of the target system and then load the data into the destination system. As organizations’ data needs grew to include more data from various sources, the number of integrations and integration types grew, making the ETL processes cumbersome to manage.

Data integration is increasingly handled by domain experts close to the data source and made available as datasets that users without a software development background can consume and visualize via an intuitive user interface. Modern enterprise data integration platforms often rely on the concept of data products to meet this new use case. This article introduces the core concepts behind this new approach to data integration.

Summary of modern data integration concepts

Data engineering has evolved in many ways, and the following modern data paradigms have greatly improved data integration.

Modern data integration concepts

Standard data file formats

The increasing volume and variety of data organizations generate have also driven the development of more advanced data formats. Formats such as XML, JSON, YAML, CSV, Avro, Parquet, and ORC, have made it easier for data integration platforms to exchange and process data from various sources. 

They have helped create more powerful and flexible data integration platforms to support multiple data integration scenarios and workloads. For example, JSON is typically used for sharing data via a Restful Application Programming Interfaces (API), making it easier to programmatically parse the commonly-formatted data payloads by different data collectors while still keeping the files human-readable. For example, the JSON payload below maps different colors to their HTML color codes.

[
	{
		color: "red",
		value: "#f00"
	},
	{
		color: "green",
		value: "#0f0"
	},
	{
		color: "blue",
		value: "#00f"
	},
	{
		color: "cyan",
		value: "#0ff"
	}
]

Sample JSON file mapping colors to their HTML codes

Data transformation and monitoring

Data transformation and monitoring tools help organizations improve the quality of data as it progresses through the integration from source to target systems.

Data transformation tools allow organizations to standardize data. The data is converted to the format of the target system, restructured to fit its schema and data model, or extended by combining it with data from additional sources. Transformation tools often cleanse the data and remove duplicates to ensure consistency or apply rules or calculations to data, mapping, or aggregation for data analysis.

Data monitoring tools track the movement and transformation of data to provide visibility into the data integration process. These tools can monitor the status of these monitoring metrics and alert users to any potential errors or issues in real-time. Proper integration monitoring helps ensure that the data being transferred is accurate and consistent to prevent data quality issues and processing delays. 

Prebuilt and no-code integration

Prebuilt, no-code data integrations are turnkey systems that allow users to connect data systems without writing code. Starting with a turnkey solution enables early success.

These integrations are typically more cost-effective than building custom integrations. They save time and effort because the integration does not have to be made from scratch. These integrations are necessary for organizations without programmers; the vendor and users who support them can fix any issues.

Vendors in the data connector market provide prebuilt connectors for many data sources and analytics systems. These prebuilt connectors are low-code solutions, unlike custom-built connectors.

Users who are not software developers can directly customize their access to data and choose from a portfolio of prebuilt integrations such as the ones on this list.

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Data products

A data product is a software product or service designed to deliver data in consistent formats to a wide audience of data consumers. Raw data in a given format, such as CSV, doesn’t constitute a data product because it lacks metadata and features that make it ready to use.

Data products have several key features. These include descriptions and annotations, which provide context; test samples; and a description of the data and how it can be used. Data products also include validations to ensure the accuracy and quality of the data as well as error management features to handle any issues that may arise. Access control is also an essential feature of data products, as it helps to ensure that only authorized users can access the data. Finally, data products should have history and audit logs to track any changes or updates to the data over time.

Data products can be consumed by various data users, including data scientists, analysts, developers, and business users. Data products should not create new copies of data and should be produced, owned, and maintained by the subject matter experts who understand the data best. 

Data products allow for collaboration and can be modified to create new derivative products. This approach to data products helps ensure that they are reliable, accurate, and easy to use, and it can provide valuable insights and information to users. The diagram below illustrates the notion of a data product as presented in the user interface of a self-service data engineering platform such as Nexla. This article explains data products in more depth.

Data catalog

A data catalog is an organized, comprehensive inentory of an organization’s data products and assets. The inventory can include data in various systems, such as databases, data lakes, operational systems, and cloud-based applications.

A data inventory is essential for supporting the use of data products because it helps users find the available data. It can also provide meaningful context and metadata about the data, such as its format, structure, and quality, helping users ensure that the data is accurate and up to date.

Developing a thorough data inventory is an investment that saves users time when performing data discovery. 

Data mesh

Data mesh architecture is a modern approach to data management that aims to solve the challenge of supporting a large number of data users within an organization without overburdening the data system. It is an evolution of data democratization, which seeks to make data more accessible within an organization.

At the center of the data mesh approach is the idea of the data product as the unit around which collaboration can happen. In traditional data management approaches, there is a tight coupling between the data at its source and the data user, which can be challenging to scale. On the other hand, data products create a loose coupling between producers and consumers of data, making it easier to support many data users. Self-service is also an essential aspect of data mesh, as it helps to reduce dependency on technical resources and allows anyone who understands data to create and manage data products. 

For example, in a traditional data warehouse, an administrator decides who should access specific data in a centralized repository – an approach that creates a logistical bottleneck. However, data owners independently determine who can view or edit their data in a data mesh. The self-hosted data is accessible to remote consumers who won’t have to copy and create duplicate storage before using it. 

Domain ownership is a component of data mesh architecture and a natural consequence of having self-service data products rather than centralized data. Self-service data products would require data to be owned by domain users rather than a central data repository administrator. This ownership model means that each domain within an organization can create and manage its data products and make certain data products available to users outside the domain with the appropriate governance in place. 

Data governance and ownership

Data meshes introduce the concept of federated data governance, which provides access control while enabling the democratization of the data. Data governance and security are essential for ensuring the proper use and protection of an organization’s data assets.

Data ownership refers to the policies, processes, and practices determining who manages and maintains data.

Data ownership and governance help organizations ensure that data is accurate, consistent, safe, and used in compliance with relevant laws and regulations. The data transformation and governance control are transferred to the domain experts with a data mesh.

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Learn more about data integration

Modern data management platforms help organizations collect, transform, store, analyze, and manage data. These platforms typically provide a range of capabilities, including data integration, data transformation, data quality management, data visualization, and data analytics. Organizations use them to gain insights from their data, support decision-making, and support data governance and compliance efforts. One of the key benefits is their ability to integrate data from a wide range of sources, including structured and unstructured data, cloud-based data, and real-time data streams.