What criteria should you use to select a big data integration framework?

When selecting a big data integration framework, consider data sources, volume, velocity, and quality alongside governance, transformation, and analysis needs. Prioritize frameworks aligning with ETL best practices and goals, emphasizing scalability and data privacy. If on AWS with healthcare workload, leverage Glue for data integration, ensure data privacy through encryption and compliance controls. Its serverless architecture scales with demand, and features like data cataloging streamline governance. Coupled with Lambda for data transformation and Redshift for analysis, it offers a solution for secure, compliant healthcare data integration.

Some of the criteria that you can use in selecting a big data integration framework are: Assess the level of vendor support, documentation, and professional services available for the framework. Consider the vendor's reputation, track record, and long-term commitment to the product, as well as the roadmap for future enhancements and updates.Evaluate the total cost of ownership, including licensing fees, infrastructure costs, maintenance expenses, and potential training requirements.Verify that the framework includes robust security features to protect sensitive data during ingestion, processing, and storage. Look for support for authentication, authorization, encryption, and compliance with industry regulations.

Example: A RDBMS for customer details (structured) - Use Qlik Replicate to establish a real-time data connection to the database, ensuring that customer information is always up-to-date in your integration platform. A log file with website activity (semi-structured) - Leverage Talend big data and apache spark to process the semi-structured website activity data. You can then use Qlik Replicate to integrate the processed data into your central data repository. Social media sentiment analysis (unstructured) - Utilize specialized tools or APIs to collect and analyze social media data. The sentiment analysis results can then be integrated with your other customer data using Qlik Replicate or Talend or similar data integration tool.

To choose the ideal big data integration framework, start by aligning your data strategy with the framework's strengths. Consider the variety of data sources and formats you need to integrate, both now and in the future.

Don't limit your vision to current needs; select a framework with proven scalability to support your organization's future growth and evolving big data ambitions.

By choosing a framework that champions data quality and governance, you'll foster trust in your data-driven insights, leading to more confident decision-making across the organization.

1. Set Business Goals: Your data integration plan should support your organization’s overall business goals—understanding those goals lays the foundation for your plan to succeed. 2. Evaluate Existing Data Infrastructure: Evaluating your existing data infrastructure and processes includes assessing the quality and cleanliness of the available data to uncover inconsistencies, redundancies, or inaccuracies. 3. Identify Stakeholders: Key stakeholders involved in the data integration strategy from across the organization should have clear roles and responsibilities. 4. Define Scope And Purpose: Clearly outline the scope, purpose, and anticipated outcomes of the data integration initiative.

For transformative insights, consider frameworks enabling advanced capabilities such as machine learning, graph analysis, and real-time event processing. These open up exciting possibilities for predictive modelling and complex data exploration.

Aligning with data integration and ETL best practices ensures your framework choice has longevity. Prioritize solutions with established design principles like modularity and reusability, promoting long-term maintainability of your data pipelines.

If you are in a cloud environment, what tools the cloud vendor provide. With all due respect to AI, I bet this AI generated content headlines match with 20 other questions on data architectures - all with the same flow - data source, format, volume, velocity etc :-) We need another AI tool to stop proliferation of these articles ( I know, I feel into this also :-) )