Data Integration

Data integration is a series of data management procedures for bringing datasets from different sources into data lakes, data warehouses, or other data stores.

It involves using data ingestion, cleansing, and transformation through data pipelines to create a single source of data for business analysis.


Challenges of data integration

The complexity of enterprise data architectures creates persistent challenges for data integration activities. For example, the data sources may be on-premises relational databases, cloud-based clickstreams, or third-party software-as-a-service (SaaS) providers. In addition, the data itself may be structured values from transactional apps or unstructured real-time data from e-commerce systems. Here are some challenges data teams must overcome in the data integration process.

Data quality:

For the final dataset to be useful, the quality of its data must be consistent. However, data quality will vary from source to source. Some sources are inherently noisy, while others have different quality standards. Engineers must evaluate each source and apply data integration methods to address missing, inaccurate, and redundant data.

Big data challenges:

Data integration would not be necessary if enterprise data sources weren’t enormous. But volume is not the only defining characteristic of big data. The variety of data businesses use has exploded, as has the velocity of real-time and streaming data.

Data security and privacy:

Complicating matters further is the sensitivity of the integrated data. Pulling in customer data makes initiatives fall afoul of various data privacy regulations. Security policies may limit access to the final dataset to certain authorized employees. Simply copying data into another location generates security issues as data replication creates another opportunity for threat actors.

Data governance:

Data integration is particularly challenging in organizations that lack robust data governance systems. When each business domain or region manages data infrastructure to its own standards, its data sources risk becoming data silos. Data integration systems can only access these resources with significant coordination with the data owners, which raises costs and delays projects.


Inevitably, source systems and target systems will suffer from compatibility issues. Some systems streamline access through on-demand APIs. Proprietary databases may require hands-on batch processing. Even when these systems support “standards” like SQL, their unique implementations require different data integration methods.

Data transformation:

Another source of variation data integration solutions must address is the structure of each source dataset, including column names, formatting, and metadata taxonomy. Data transformation pipelines must convert these elements and more to meet the single standard of the destination. Engineers must repeat this time-consuming process for every source — and then monitor each source for changes that could corrupt the integration pipelines.

Data latency:

Integration projects may support real-time use cases. Dashboards and other data products provide decision-makers with live updates of critical metrics. In the case of application integration, data from disparate sources enters a single feed for an organization’s operational systems. Both cases rely on consistent, low-latency performance from integration pipelines.


Larger and more diverse data sources can yield valuable insights to drive growth and innovation — if integration systems can keep up. Scalability is a serious concern for data teams. On-premises storage is difficult to maintain at scale. Proprietary data warehousing systems require expensive compute and storage investments. Data lakes require constant attention to prevent them from becoming data swamps.

Data lineage:

Data lineage describes where data came from and what happened to it before arriving in the final dataset. In most cases, multiple transformations take place between data generation and integration. For example, a retail sales dataset aggregated by store will not match a dataset aggregated by product that includes online sales. Analysts must understand the lineage of each source dataset to avoid making assumptions that produce inaccurate results.

Cost management:

Because of these challenges, complex data integration projects are expensive. Personnel costs are significant contributors to rising integration budgets. Few people know how to navigate enterprise storage infrastructures and develop integration pipelines. Data engineers command high salaries for their specialized skills, limiting the size of data teams and their availability.

Data integration use cases

The benefits of data integration power use cases across every industry. Giving business intelligence analysts a holistic view of enterprise data reinforces data-driven decision-making. Data integration platforms also support machine learning apps that create new sources of business value. Here are a few use cases for data integration:

Customer 360 View: Integration combines customer data from CRM, transactional, marketing, and other sources to create a 360-degree view of the company’s customer relationships.

Supply Chain Management: Combining data from in-house and third-party sources gives companies an end-to-end perspective of their supply chains to optimize logistics and inventory costs.

Healthcare Data Integration: From patient care to drug development, modern healthcare depends on the insights generated from disparate data sources.

Financial Services: Banks and other financial institutions rely on multiple information sources to enforce know-your-customer and anti-money-laundering policies.

E-commerce and Retail: Today’s retailers must collect data from brick-and-mortar stores, apps, e-commerce sites, loyalty programs, and other sources to understand how customers research, buy, and return products.

Data integration tools

Choosing the right data integration tools depends on the types of data integration used and the nature of the final data set.


Extract, transform, and load (ETL) pipelines pull data from a source and make it fit for purpose before loading the data into a destination.

ETL tools are more appropriate when the final dataset is a common resource for multiple users.


Extract, load, and transform (ELT) pipelines load the data into the destination where subsequent transformation takes place.

ELT tools work best with data blending and other projects that depend on rapid turnaround.

Data integration and data virtualization

Starburst’s modern data lake analytics platform is the foundation for streamlined data integration systems. Rather than creating yet another data store, Starburst leaves data at the source and uses data virtualization to create, in effect, a single source for all data in the enterprise.

Deploy anywhere with a unified view

Traditionally, data warehouses and other centralization efforts sought to compile a single source of truth for analysts and other data consumers. Starburst creates a single source of access to data stored anywhere in the company. Providing analysts with a unified view of business data democratizes access and frees many business needs from the limited resources of data teams.

Ecosystem of connectors

Starburst offers connectors to over fifty enterprise data sources, including Microsoft SQL Server, Amazon Redshift, and Splunk. These connectors map Starburst’s single interface to each source’s types, configuration properties, SQL dialects, functions, and other features.

As a result, users performing routine analysis can integrate data from different sources without the deep expertise of data engineers. They can independently discover and extract the data they need.

For the same reason, connectors also simplify life for data teams when working on complex projects like data migrations. Starburst’s interface lets engineers discover data and build complex pipelines faster.


Starburst delivers much-needed scalability by decoupling compute from storage. All data remains at the source and is only accessed during query run-time, allowing data managers to optimize their storage investments. Compute no longer requires the capital expense needed to meet peak demand. Instead, compute becomes an elastic capital expense.

Starburst’s performance is similarly scalable. Built upon Trino’s open source, massively parallel query engine, Starburst adds performance and cost optimization features like dynamic filtering and pushdown queries.


Starburst provides a holistic view of data sources across the enterprise. This single point of access lets users at different skill levels, from data scientists to business intelligence analysts to general users, incorporate data analysis in their decision-making processes.

Business leaders do not have to wait for data teams to build a data integration pipeline. Instead, their analysts can discover and integrate data to deliver the answers leaders need to make effective decisions.

Security and compliance

Starburst includes role-based and attribute-based access controls that determine what data each user may see. These rules streamline compliance and make it easier to enforce data privacy practices.

The European Union’s GDPR, for example, encourages companies to keep data about EU residents in Europe and restricts who may view personally identifiable information. Starburst lets administrators apply granular rules at the schema, table, column, and row levels. When analysts in the United States access European data, Starburst access rules will aggregate the data at the source so the analysts cannot see individual information.

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