What are the latest data integration trends that you need to know?

One example of data integration is HL7 EHR ( electronic health records) and EMR ( electronic medical records) data in cloud. What makes this integration special is due to its inherent PHI/PII nature of data and HL7 standards which makes the data model unique. There are multiple integration tools available but we will investigate HDE ( Healthcare Data Engine) in GCP. HDE V2 uses: 1. Kubernetes for real time HL7 data ingestion. 2. Dataflow for data processing. 3. Data Mapper IDE. 4. FHIR store ( Not Firestore). 5. BigQuery. HDE is an easy to deploy package from google that makes this entire deployment process easy, the data mapping tool provides easy way to map data types ans attributes.

Relevant points has been already covered by the experts here. I have some additional points to consider from Enterprise Integration Landscape point of view. Data Integration toolkits evolution within each organisation is based on the specific ecosystem they have in place. DI Toolkit must support at any point in time a mix of integration patterns required for the existing legacy as well as modern platforms. Since there is lot of focus on AI Use Case supported by DI, the maturity of the products and organizing to leverage these Cloud Services must be accounted. Having a Hybrid Integration Platform model (having Event Streaming, API's, Batch, EFT/SFTP/MFT, etc) based on native Cloud Services/ iPaaS is a good choice in 3-5 year horizon.

You should strongly consider that every sustainable data-integration company now have ETL/ELT/Streaming/Microbatch/etc.-framework based on popular open source solutions. Data integration in 2024 is commodity, you should not choose it beforehand, rather look for actuality of tech used by your data integration partners and 3-5Y TCO of services required to maintain your data pipelines.

Today's world is cloud first and cloud provider (AWS, Azure & Google) are providing innovative solutions to address the growing data integration needs which can be scaled easily without any extra effort. First step is to study the data ingestion services provided by the cloud provide

Data integration pipelines, a prominent trend, automate the ETL (extraction, transformation, loading) process, enhancing efficiency, quality, and reliability. They minimize manual errors, enable real-time data delivery, and ensure robust data governance. Utilizing tools like Apache Airflow, Apache NiFi, AWS Glue, Azure Data Factory, and Google Cloud Dataflow, these pipelines seamlessly integrate with data lakes, warehouses, marts, and visualization tools. This evolution reflects a commitment to optimizing data flow, empowering organizations to harness its full potential.

The latest trends in data integration pipelines are: The rise of real-time data pipelines. Businesses need to be able to make decisions quickly in today's fast-paced world, and real-time data pipelines enable them to do just that. Real-time data pipelines ingest, process, and analyze data as soon as it is generated, allowing businesses to gain insights and take action immediately. A growing importance of data quality and data governance. As businesses rely more and more on data-driven decision-making, it is essential that their data is accurate, consistent, and reliable. The impact of machine learning and AI on data pipeline design.

Data quality needs to be looked at with different lens.? Traditional DQ in a single data warehouse or structured DB is easy to implement and maintain, but creative solutions need to be explored when your data landscape includes several source systems and middleware orchestrating your data integration. This seems to be uncharted territory currently.

Data quality management is crucial to ensure, as a company, you make the right decisions and expand your strategy. Protecting its integrity and safeguarding it against cyberattacks is essential to maximise its impact. Successful use of data can drive automation and optimisation of processes - ultimately reducing administrative costs. And from a customer perspective, good data enhances the experience and enables a company to be proactive instead of reactive to customer needs.

The rise of real-time data virtualization. Real-time data virtualization enables businesses to access and analyze data from a variety of sources in real time, without having to create and maintain expensive data warehouses. This is essential for businesses that need to make data-driven decisions quickly. The growing importance of data governance and security. As businesses rely more and more on data virtualization, it is essential to have robust data governance and security measures in place

Data Visualization essenstially and broadly spoken in terms of Descriptive Analytis which means asking data what happened in the past. Various BI dashboard tools are avilable in the market which can server the purpose of storytelling which is most sinequonon for any data driven organisation to take right, timely and accurate decisions.

Increasing adoption of cloud-native DIaaS solutions, leveraging the benefits of cloud infrastructure for scalability, flexibility, and ease of integration with other cloud services.

With rapidly increasing cloud architecture, DIaaS is gaing popularity day by day. It is easy to implement(using cloud services) , flexible (DIaaS is more scalable than on-premises alternatives), Cost Effective ?(helps to optimize data management and processing costs) and acting as Single source of truth.

I think to need know about data standard and harmonization to build right data that your produc will use and your architecture will available to use for cases of analytics and exploring. The key point based in mangament of metadata into business and sources as well their data quality to know how it will be integrate.

Micro ETLs, MQTT, Behavioural analytics data, understanding threat vectors for security applications would be some of the discussion points.

Given AI’s growing role in processing corporate data, incorporating AI-focused tasks into data integration is essential. Essential tasks include data cleaning and preprocessing, ensuring data quality for AI algorithms and feature engineering, and optimizing data attributes for AI use. Data annotation is crucial for AI’s accurate disease detection training in sectors like healthcare. Embedding these AI-preparatory tasks within data integration pipelines aligns with trends like Cloud-based data integration, enhancing AI's effectiveness and ensuring data readiness for advanced applications.