The Data Engineering Decision Guide to Data Integration Tools
Actian Corporation
March 15, 2024
With organizations using an average of 130 apps, the problem of data fragmentation has become increasingly prevalent. As data production remains high, data engineers need a robust data integration strategy. A crucial part of this strategy is selecting the right data integration tool to unify siloed data.
Assessing Your Data Integration Needs
Before selecting a data integration tool, it’s crucial to understand your organization’s specific needs and data-driven initiatives, whether they involve improving customer experiences, optimizing operations, or generating insights for strategic decisions.
Understand Business Objectives
Begin by gaining a deep understanding of the organization’s business objectives and goals. This will provide context for the data integration requirements and help prioritize efforts accordingly. Collaborate with key stakeholders, including business analysts, data analysts, and decision-makers, to gather their input and requirements. Understand their data needs and use cases, including their specific data management rules, retention policies, and data privacy requirements.
Audit Data Sources
Next, identify all the sources of data within your organization. These may include databases, data lakes, cloud storage, SaaS applications, REST APIs, and even external data providers. Evaluate each data source based on factors such as data volume, data structure (structured, semi-structured, unstructured), data frequency (real-time, batch), data quality, and access methods (API, file transfer, direct database connection). Understanding the diversity of your data sources is essential in choosing a tool that can connect to and extract data from all of them.
Define Data Volume and Velocity
Consider the volume and velocity of data that your organization deals with. Are you handling terabytes of data per day, or is it just gigabytes? Determine the acceptable data latency for various use cases. Is the data streaming in real-time, or is it batch-oriented? Knowing this will help you select a tool to handle your specific data throughput.
Identify Transformation Requirements
Determine the extent of data transformation logic and preparation required to make the data usable for analytics or reporting. Some data integration tools offer extensive transformation capabilities, while others are more limited. Knowing your transformation needs will help you choose a tool that can provide a comprehensive set of transformation functions to clean, enrich, and structure data as needed.
Consider Integration with Data Warehouse and BI Tools
Consider the data warehouse, data lake, and analytical tools and platforms (e.g., BI tools, data visualization tools) that will consume the integrated data. Ensure that data pipelines are designed to support these tools seamlessly. Data engineers can establish a consistent and standardized way for analysts and line-of-business users to access and analyze data.
Choosing the Right Data Integration Approach
There are different approaches to data integration. Selecting the right one depends on your organization’s needs and existing infrastructure.
Batch vs. Real-Time Data Integration
Consider whether your organization requires batch processing or real-time data integration—they are two distinct approaches to moving and processing data. Batch processing is suitable for scenarios like historical data analysis where immediate insights are not critical and data updates can happen periodically, while real-time integration is essential for applications and use cases like Internet of Things (IoT) that demand up-to-the-minute data insights.
On-Premises vs. Cloud Integration
Determine whether your data integration needs are primarily on-premises or in the cloud. On-premises data integration involves managing data and infrastructure within an organization’s own data centers or physical facilities, whereas cloud data integration relies on cloud service providers’ infrastructure to store and process data. Some tools specialize in on-premises data integration, while others are built for the cloud or hybrid environments. Choose a tool that depends on factors such as data volume, scalability requirements, cost considerations, and data residency requirements.
Hybrid Integration
Many organizations have a hybrid infrastructure, with data both on-premises and in the cloud. Hybrid integration provides flexibility to scale resources as needed, using cloud resources for scalability while maintaining on-premises infrastructure for specific workloads. In such cases, consider a hybrid data integration and data quality tool like Actian’s DataConnect or the Actian Data Platform to seamlessly bridge both environments and ensure smooth data flow to support a variety of operational and analytical use cases.
Evaluating ETL Tool Features
As you evaluate ETL tools, consider the following features and capabilities:
Data Source and Destination Connectivity and Extensibility
Ensure that the tool can easily connect to your various data sources and destinations, including relational databases, SaaS applications, data warehouses, and data lakes. Native ETL connectors provide direct, seamless access to the latest version of data sources and destinations without the need for custom development. As data volumes grow, native connectors can often scale seamlessly, taking advantage of the underlying infrastructure’s capabilities. This ensures that data pipelines remain performant even with increasing data loads. If you have an outlier data source, look for a vendor that provides Import API, webhooks, or custom source development.
Scalability and Performance
Check if the tool can scale with your organization’s growing data needs. Performance is crucial, especially for large-scale data integration tasks. Inefficient data pipelines with high latency may result in underutilization of computational resources because systems may spend more time waiting for data than processing it. An ETL tool that supports parallel processing can handle large volumes of data efficiently. It can also scale easily to accommodate growing data needs. Data latency is a critical consideration for data engineers, because it directly impacts the timeliness, accuracy, and utility of data for analytics and decision-making.
Data Transformation Capabilities
Evaluate the tool’s data transformation capabilities to handle unique business rules. It should provide the necessary functions for cleaning, enriching, and structuring raw data to make it suitable for analysis, reporting, and other downstream applications. The specific transformations required can include: data deduplication, formatting, aggregation, normalization etc., depending on the nature of the data, the objectives of the data project, and the tools and technologies used in the data engineering pipeline.
Data Quality and Validation Capabilities
A robust monitoring and error-handling system is essential for tracking data quality over time. The tool should include data quality checks and validation mechanisms to ensure that incoming data meets predefined quality standards. This is essential for maintaining data integrity and accuracy, and it directly impacts the accuracy, reliability, and effectiveness of analytic initiatives. High quality data builds trust in analytical findings among stakeholders. When data is trustworthy, decision-makers are more likely to rely on the insights generated from analytics. Data quality is also an integral part of data governance practices.
Security and Regulatory Compliance
Ensure that the tool offers robust security features to protect your data during transit and at rest. Features such as SSH tunneling and VPNs provide encrypted communication channels, ensuring the confidentiality and integrity of data during transit. It should also help you comply with data privacy regulations, such as GDPR or HIPAA.
Ease of Use and Deployment
Consider the tool’s ease of use and deployment. A user-friendly low-code interface can boost productivity, save time, and reduce the learning curve for your team, especially for citizen integrators that can come from anywhere within the organization. A marketing manager, for example, may want to integrate web traffic, email marketing, ad platform, and customer relationship management (CRM) data into a data warehouse for attribution analysis.
Vendor Support
Assess the level of support, response times, and service-level agreements (SLAs) provided by the vendor. Do they offer comprehensive documentation, training resources, and responsive customer support? Additionally, consider the size and activity of the tool’s user community, which can be a valuable resource for troubleshooting and sharing best practices.
A fully managed hybrid solution like Actian simplifies complex data integration challenges and gives you the flexibility to adapt to evolving data integration needs.
The best way for data engineers to get started is to start a free trial of the Actian Data Platform. From there, they can load their own data and explore what’s possible within the platform. You can also book a demo to see how Actian can help automate data pipelines in a robust, scalable, price-performant way.
For a comprehensive guide to evaluating and selecting the right Data Integration tool, download the ebook Data Engineering Guide: Nine Steps to Select the Right Data Integration Tool.
