What is data integration?

Data integration is the process of combining data from multiple systems into a unified, reliable view. It brings together information from databases, applications, event streams, files, APIs and third-party platforms so organizations can work with data as a whole rather than in isolated pockets. As data volumes grow and systems become more fragmented, data integration has become a foundational capability for analytics, AI and decision-making.

Most organizations rely on many systems that generate essential information. CRM platforms store customer interactions, ERP systems manage financial transactions, marketing tools track digital engagement and support applications log service issues. Without integration, this information stays siloed, reducing trust, slowing decisions and limiting visibility into what is happening across the business.

Modern integration practices address these challenges by creating governed, centralized pipelines for collecting, transforming and unifying data. The result is a consistent dataset that teams can use confidently across reporting, business intelligence, machine learning and real-time applications.

How data integration works: core processes

Data ingestion: bringing data into the system

Data ingestion is the entry point to integration. It focuses on capturing data from source systems and moving it to a central environment such as a data lake, data warehouse or lakehouse. This may involve pulling data from relational databases, SaaS applications, IoT devices, message queues, log files or partner systems.

A strong ingestion layer keeps integration scalable and reliable by supporting large volumes, heterogeneous formats and evolving schemas, and by maintaining pipeline resilience as sources fluctuate or grow.

Many organizations use connectors, change data capture (CDC) patterns and event-based pipelines to keep ingestion efficient and responsive. Tools like Lakeflow Connect, part of Databricks Lakeflow, help streamline this work by providing prebuilt, high-performance connectors that simplify ingesting data from operational databases and SaaS applications.

Real-time vs. batch ingestion

Ingestion typically operates in one of two modes, depending on latency and freshness requirements:

• Batch ingestion loads data at scheduled intervals, such as every hour or night. It is cost-efficient and suitable for traditional reporting, budgeting cycles, regulatory submissions and historical analytics.
• Real-time ingestion captures and processes data continuously as events occur. It powers applications such as fraud detection, personalization engines, real-time analytics dashboards and automated alerts.

Organizations often use both modes to balance performance and analytical needs. Real-time pipelines provide immediate insights, while batch jobs efficiently refresh large volumes of historical data.

Collecting from diverse source systems

Modern environments rely on distributed, cloud-native and hybrid systems, so integration must handle a wide variety of sources efficiently, including:

• Operational databases (MySQL, PostgreSQL, SQL Server)
• Cloud data stores
• SaaS applications such as Salesforce, ServiceNow, Workday and Adobe
• Streaming platforms such as Apache Kafka
• Files and object storage including Parquet, JSON and CSV
• APIs that emit structured and unstructured data
• Machine-generated sources such as IoT and sensor streams

Integration pipelines must handle these diverse formats and protocols efficiently to maintain a complete picture of business operations.

Data transformation: cleaning and standardizing data

Once data is ingested, it must be prepared for analysis. Raw data often arrives with inconsistencies in format, structure and quality, so it must be cleaned and standardized before downstream use. These steps ensure the resulting dataset is consistent and reliable across analytics and machine learning workloads.

Data cleansing and validation

Data cleansing and validation are key parts of the transformation process. Cleansing improves reliability by resolving issues such as duplicate records, incorrect data types, inconsistent formatting, missing values and outliers that may indicate incorrect entries.

Validation then confirms that the transformed data remains accurate as source systems evolve. Automated checks surface issues such as schema drift, unexpected nulls or shifts in field behavior before they affect downstream processes.

Converting data into consistent formats

Standardizing data ensures that information from different systems aligns to a shared structure and set of definitions. This work includes unifying schema elements, standardizing record layouts, aligning naming conventions and converting values into consistent, interpretable formats so downstream analytics and machine learning models can operate reliably.

Loading data: storage options and architectures

Loading is the last stage of the integration process, where transformed data is moved into a storage environment for analytics and application use. After cleansing and standardization, data must be stored where teams can easily query and apply it. Storage architecture directly affects scalability, performance and downstream usability, and each option fits different needs within the integration process.

Data warehouses

Data warehouses support business intelligence and structured analytics at scale. They store consistent, curated data optimized for SQL queries, dashboards and compliance-driven reporting. Warehouses are ideal for workloads that rely on stable schemas and well-governed datasets.

Data lakes

Data lakes store raw, semi-structured and unstructured data at lower cost, supporting flexible exploration, large-scale analytics and machine learning. They allow organizations to capture all enterprise data — not just structured records — and make it available for downstream transformation.

For guidance on designing and managing these environments, see the comprehensive Databricks guide to data lakes best practices.

Lakehouses

A lakehouse architecture incorporates the strengths of both data lakes and warehouses. It combines low-cost object storage with performance optimizations for SQL workloads, allowing organizations to unify their analytics and AI pipelines in a single environment. By reducing infrastructure overlap, lakehouses simplify governance and accelerate data-driven initiatives.

Data integration in action

Consider an organization where customer-related data is spread across several departments. Sales manages accounts and pipelines in a CRM system. Marketing tracks user engagement and campaign performance in marketing automation tools. Support logs tickets and customer interactions in a helpdesk platform.

Without integration, these systems provide only partial views of customer behavior, making it difficult to assess broader patterns or performance. Analysts must manually reconcile conflicting or incomplete records, increasing the likelihood of inaccurate conclusions.

With an integrated pipeline, teams can bring this data together more effectively:

• Ingestion pulls data from CRM, marketing and support systems through connectors.
• Transformation aligns customer identifiers, standardizes schemas and resolves inconsistencies.
• Loading writes the unified records into a governed layer within a lakehouse, giving all teams access to consistent, analytics-ready information.

When data from different departments is unified in this way, teams can answer questions that span the entire customer lifecycle, such as which marketing campaigns influence sales opportunities, whether customers with frequent support tickets have lower renewal rates or which segments respond best to specific product features.

By replacing isolated spreadsheets and disconnected pipelines with a shared, governed data layer, organizations gain a clearer view of customer journeys. This shared visibility supports more accurate forecasting and enables better personalization across all customer-facing functions.

Common techniques and technologies for data integration

ETL (extract, transform, load)

ETL is a long-standing data integration approach in which data is extracted from source systems, transformed to meet business requirements and then loaded into a target environment. It is widely used for regulatory reporting, financial analytics and other workflows that require highly curated, structured data.

ETL remains especially valuable when transformations must occur before data enters the target system, ensuring that downstream consumers receive consistent, predefined schemas. For a deeper overview of ETL concepts and implementation patterns, see the Understanding ETL technical guide from O’Reilly.

ELT (extract, load, transform): transforming data after loading

ELT flips the sequence by loading raw data into the target system first and then transforming it there. Because cloud-based systems offer elastic compute, ELT can be more efficient, scalable and flexible. It also preserves raw data, allowing data teams to revisit or repurpose datasets later without re-extraction.

Organizations often use ETL for highly regulated or curated datasets and ELT for exploratory analytics or large-scale workloads. Learn more about the difference between ETL and ELT.

Data virtualization

Data virtualization enables users to query data across disparate systems without physically moving it, providing fast access to distributed information. It is useful when:

• Data must remain on-premises due to regulatory constraints
• Teams need real-time access to operational data
• Moving large datasets is cost-prohibitive

While virtualization improves access to distributed sources, it is less suitable for compute-intensive analytics or large-scale ML training, which perform best with local processing and optimized storage formats.

Data federation

Data federation allows users to run queries across multiple source systems at query time, with each system processing its portion of the request. Instead of abstracting or optimizing access to the data, federation coordinates queries across systems and combines the results into a single view.

This approach is useful when data must remain in place due to regulatory or operational constraints or when teams need cross-system insights without building new ingestion pipelines. Because performance depends on the underlying source systems, federation is generally less suited for complex analytics or compute-intensive workloads.

Data replication

Replication synchronizes copies of data across multiple systems to ensure availability and consistency. It can support:

• Disaster recovery
• Read-optimized analytical systems
• Distributed applications that rely on up-to-date information

Replication may be continuous or scheduled, depending on latency requirements.

Data Orchestration

Beyond individual integration techniques, data orchestration ensures that pipelines run reliably at scale. Data orchestration coordinates the execution, scheduling and monitoring of data integration workflows, making sure ingestion, transformation and loading steps run in the correct order, handle dependencies properly and recover from failures. As data environments grow more complex, orchestration becomes essential for operating pipelines that span multiple systems, processing modes and teams.

Effective orchestration supports capabilities such as dependency management, retries, alerting and observability, helping teams operate integration workflows at scale.

Lakeflow Jobs supports orchestration for data integration and ETL workflows by providing a unified way to schedule, manage and monitor data pipelines across the Lakehouse.

Data quality and reliability

Ensuring high data quality is essential for trustworthy analytics and reliable downstream systems. Integrated data often feeds reports, dashboards and machine learning models, so quality must be measured and maintained as data sources and pipelines evolve.

Data quality metrics

Organizations use several core metrics to assess whether integrated data is ready for analytics and operational use:

• Accuracy: Values reflect real-world truth, such as correct customer addresses or valid transaction amounts.
• Completeness: Required fields are populated and no important records are missing.
• Consistency: Data remains aligned across systems, formats and time periods without conflicting values.

Quality assurance processes

Quality assurance plays a critical role in keeping integrated data accurate and reliable as systems evolve. It includes data validation and error handling, which ensure that transformed data meets expected standards before it is loaded into downstream environments.

Validation checks confirm that schemas, formats and business rules remain intact throughout the data pipeline. With Databricks Lakeflow Structured Data Pipelines (SDP),expectations enable teams to apply quality constraints that validate data as it flows through ETL pipelines, providing greater insight into data quality metrics while allowing you to fail updates or drop records when detecting invalid data. These error-handling workflows prevent bad or incomplete data from entering analytics or operational systems, ensuring downstream consumers can trust the data they’re working with.

Monitoring and alerting systems extend these safeguards by detecting unexpected changes in data volume, schema structure or pipeline behavior. Alerts allow teams to respond quickly to anomalies and resolve issues before they impact consumers.

Together, these processes maintain the stability of integration pipelines and support consistent, high-quality data across the organization.

Governance and security

While data quality focuses on correctness and reliability, governance and security define how integrated data is managed, protected and used responsibly across the organization. Strong data governance establishes trust by ensuring access, usage and compliance are clearly defined and enforced.

Implementing governance frameworks

Governance frameworks define how data is collected, stored, accessed and managed throughout its lifecycle. Clear, enforceable frameworks help teams maintain consistency as data volumes grow and new systems are added.

Defining and enforcing data policies

Effective governance relies on well-defined policies that guide how data is handled across teams and platforms. Common policy areas include:

• Naming conventions and schema standards
• Data retention and archival practices
• Handling of sensitive or regulated data
• Version control and lifecycle management

When enforced consistently, these policies help reduce fragmentation and ensure data is managed responsibly across the organization.

Security and access controls

Security is a foundational element of data governance. It establishes the protections and access controls that safeguard sensitive data, prevent unauthorized use and help organizations meet compliance requirements. Key security capabilities include:

• Authentication and identity management
• Role-based access controls
• Encryption at rest and in transit
• Privilege separation
• Secure data sharing frameworks

Together, these controls help organizations protect integrated data while enabling secure, governed access for analytics and operations.

Common data integration challenges

As integration pipelines grow in scope and complexity, organizations encounter a common set of practical challenges across scale, architecture and ownership. The following challenges illustrate common friction points and the approaches organizations use to address them:

• Inconsistent formats: Standardizing schemas and metadata resolves mismatches.
• Large data volumes: Distributed compute and autoscaling enable efficient processing.
• Complex hybrid or multicloud architectures: Federation, virtualization and unified governance simplify cross-environment access.
• Siloed ownership: Clear roles, shared standards and centralized orchestration create consistency and reduce fragmentation.
• Evolving source systems: Automated validation and schema-aware pipelines prevent downstream errors.

With a modern integration strategy, these challenges become manageable. Unified data engineering tools such as Databricks Lakeflow help organizations simplify data integration and ETL by bringing ingestion, transformation and orchestration together in a single environment.

Choosing a data integration platform

Addressing these integration challenges requires a platform that can operate reliably across growing data volumes, complex architectures and governance requirements.

Scalability and performance

Selecting a data integration platform involves understanding how well its capabilities align with both immediate priorities and future demands. A key consideration is how well the platform can scale as data volumes and workloads increase.

Important factors include high-throughput ingestion, low-latency processing, efficient schema management, elastic compute for burst workloads and support for both structured and unstructured data. Cloud-native platforms excel in scalability because they separate storage and compute, enabling autoscaling as demand fluctuates.

Real-time requirements

If a use case requires immediate insights, the platform should support event-driven ingestion, low-latency processing, streaming-to-table pipelines and automatic recovery from failures. These capabilities enable real-time applications such as personalized recommendations, financial monitoring and operational alerting.

Cloud vs. on-premises considerations

Selecting between cloud, on-premises or hybrid deployment models depends on factors such as compliance and data sovereignty requirements, existing infrastructure investments, latency constraints, team skill sets and total cost of ownership. Many organizations choose hybrid approaches, keeping sensitive or regulated data on-premises while using cloud platforms for scalable analytics.

Security, governance and metadata capabilities

A strong integration platform must support centralized governance. Essential features include access control, metadata management, data lineage visibility, encryption at rest and in transit, fine-grained permissions for sensitive fields and audit logs for compliance. Effective governance not only protects data but also builds confidence in the reliability and transparency of the datasets used across the organization.

Conclusion

Data integration is the foundation of modern data and AI strategies. By unifying data across the organization, it creates a consistent dataset that supports analytics, machine learning and operational intelligence. This unified view enables data-driven decision-making by giving teams reliable, timely information.

The impact of integration extends beyond technical efficiency. A connected data environment strengthens collaboration, reduces redundancies and reveals insights that siloed systems obscure. When departments work from the same trusted data, they can act with greater confidence and speed.

Organizations can begin integration gradually by assessing existing silos, identifying high-impact opportunities and consolidating a few critical sources. As pipelines mature and systems become more complex, strong integration becomes essential for driving productivity, innovation and long-term competitive advantage.

To learn more about the architectural principles that support scalable integration, explore free, self-paced Databricks training: Get started with Lakehouse Architecture.

For implementing data integration and ETL on this architecture, Databricks Lakeflow provides a unified data engineering solution.

Frequently asked questions

What is data integration?

Data integration is the process of combining data from different sources into a unified view to support analysis, reporting and decision-making. It involves extracting data from various systems, transforming it into a consistent format and loading it into centralized environments such as data warehouses, data lakes or lakehouses.

Why is data integration important for organizations?

Data integration helps organizations break down silos, improve data quality and gain comprehensive insights across operations. It enables better decision-making, enhances operational efficiency and machine learning. By unifying data into a reliable foundation, integration also helps organizations remain competitive as data-driven practices expand.

What are the main types of data integration techniques?

Common integration techniques include ETL, ELT, data virtualization (creating a unified view without moving data), data replication (ensuring availability through duplicate copies) and data federation for querying data across multiple systems.

What challenges do organizations face with data integration?

Organizations often struggle with data quality issues, fragmented or legacy data sources, integrating information from multiple systems, handling large data volumes and maintaining strong security and governance. Modern integration tools, automation and well-defined governance practices help address these challenges and improve long-term reliability.