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

Integrating data into cloud has some challenges: you need to know how your provider work and how is organized. In aws you have many services that can be deployed in a Region, AZ (availavility zone),etc. After understand that, you should know some IAM funcionality to get access into a service and apply security. Cloud offers a lots of services that you can use on demand as you need

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.

In my experience running a store that sells cool gadgets and keeping track of what's sold was a bit like spinning plates – fun but confusing. We now have Data Integration Pipelines, and they're like magic! When someone buys something, our systems instantly update everything everywhere. It's so fast; we can take a quick break for coffee. And the best part? It saves us money, helps us make customers really happy, and lets our team focus on cool gadgets, not boring data stuff. It's like a three-part superhero team – time, money, and happy customers, making our business a real-time party! Data Integration Pipelines are the real superheroes.

When it comes to data integration pipeline, most commonly known as data pipeline or data engineering pipeline.One of the primary purpose of integration theese daya is to build an Enterprise Data Hub (EDH) so called Data Lake or Lakehouse to maintain ACID (Atomicity, Consistency, Isolation, and Durability) which enables enterprise and organisation to create AI/Ml as well as BI applications.

In recent years, the field of Data Integration has been undergoing major transformations due to both technological progress and changing business needs. One of the key trends is the transition to cloud solutions. The cloud allows organizations to efficiently scale resources and simplify integration by providing both infrastructure and services based on the as-a-service model. Another trend is the introduction of automation and orchestration, which reduces the number of manual operations and improves the accuracy of integration processes. With automation tools, companies can set up and manage data pipelines with less effort.

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 use of artificial intelligence (AI) and machine learning (ML) to automate and scale DQM tasks. AI and ML can be used to identify and resolve data quality issues more quickly and efficiently than traditional methods. For example, AI can be used to automatically identify missing values, duplicate records, and other anomalies in data. ML can be used to build models that can predict and prevent data quality problems. The growing importance of data quality for regulatory compliance. Many industries are now subject to regulations that require them to maintain high levels of data quality. For example, the General Data Protection Regulation (GDPR) requires businesses to collect, use, and store personal data in a compliant manner.

In data integration, a notable trend is the intensified focus on data quality management. This strategic emphasis ensures data accuracy, consistency, completeness, and alignment with intended purposes, impacting performance, usability, and overall value. Activities like profiling, cleansing, validation, enrichment, and auditing, supported by tools such as rules, metrics, dashboards, and frameworks, define data quality management. Reinforced by governance policies, it delineates roles, responsibilities, and procedures. This deliberate commitment reflects a recognition of data integrity's pivotal role in shaping meaningful insights and fostering informed decision-making.

Unit tests, ai and ml testing models and good old fashioned manual detection. Three core principles I use in my day to day. Make sure your unit tests are fair and don't just suit the data. Use ml to reinforce these tests with large data comparisons. And finally get the data output and spend the time to make sure it's doing what you believe it is. If you pass all three then it's probably time to move towards the user testing stage.

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.

A new super trend in data visualization is the use of large model languages. You can give the model a set of data and it will identify the most significant variables and build a dashboard with the main insights and trends. The specialist and LLM interact with each other in Copilot mode. In the coming years, chatbots assistants for building dashboards and reports will appear in all BI products

Slowly we are heading the direction where data virtualisation would become unavoidable. It adds the flexibility of right level of insights to the data consumers and visibility of enterprise data that was never possible in this way before.

Increasing adoption of cloud-based data virtualization solutions to leverage the scalability, flexibility, and cost-effectiveness of cloud infrastructure. Also, growing emphasis on empowering business users with self-service data virtualization tools, reducing the dependency on IT for data access.

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.

Self Service BI or Data As A Service is the need of the hour for any data centric and data driven organzation. This is the current ask from the leadership team in most of the organisations to enable business and reduce or minimize IT reliance. Of course it needs to foster a cultural change or solid training amongst the stakeholders to learn and understand the little bit of self service tools like Tableau, Power BI but in the end it has enormous benefits and cost saving.

In real word we deal with incomplete or changing requirements, lack of capacity, changes of architecture etc. It’s important to find tradeoff between standards (and best practice) and some custom stuff, between using existing tools and writing custom ones, between flexibility and simplicity, cost and quality... Often there is no obvious path. The skill of finding a good tradeoff is as important as all that technical stuff. Nobody needs an ideal solution from technical perspective for infinite price. So, I prefer to start with solutions that can be easily transformed into something else and focus on logics rather than on tools.