Data Integration Solutions

𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗜𝘀 𝗕𝗿𝗼𝗸𝗲𝗻—𝗛𝗲𝗿𝗲’𝘀 𝗛𝗼𝘄 𝘁𝗼 𝗙𝗶𝘅 𝗜𝘁 For years, we got away with simple pipelines and predictable data sources. Not anymore. Social media, IoT devices, SaaS apps, real-time streaming—data today is a 𝘄𝗶𝗹𝗱 𝗺𝗲𝘀𝘀. I worked on a project where the client relied on 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝗧𝗟 for a rapidly growing ecosystem of sources. It began to collapse under its own weight—𝘀𝗹𝗼𝘄 𝗾𝘂𝗲𝗿𝗶𝗲𝘀, 𝗼𝘂𝘁𝗱𝗮𝘁𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗮𝗻𝗱 𝘁𝗼𝘁𝗮𝗹 𝗰𝗵𝗮𝗼𝘀. We had to rethink everything. 𝗠𝗼𝗱𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗱𝗲𝗺𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗿𝗻 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀. Here’s what actually works today: ⭘ 𝗕𝗮𝘁𝗰𝗵 𝘃𝘀. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 ✓ 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Ideal for batch processing when structure is predictable. ✓ 𝗘𝗟𝗧 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗟𝗼𝗮𝗱, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺) – Offloads transformation to cloud-based compute engines, leveraging data lakes and scalable storage. ⭘ 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 & 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 ✓ 𝗖𝗗𝗖 (𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗮𝘁𝗮 𝗖𝗮𝗽𝘁𝘂𝗿𝗲) – Captures and streams only the delta, enabling real-time analytics and replication. ✓ 𝗣𝘂𝗯𝗹𝗶𝘀𝗵/𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 – A push-based model for event-driven integrations, essential for microservices and decoupled architectures. ⭘ 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 & 𝗩𝗶𝗿𝘁𝘂𝗮𝗹𝗶𝘀𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 ✓ 𝗗𝗮𝘁𝗮 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Queries data 𝗮𝗰𝗿𝗼𝘀𝘀 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 without centralising it, reducing latency in distributed architectures. ✓ 𝗗𝗮𝘁𝗮 𝗩𝗶𝗿𝘁𝘂𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 – Provides a 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗹𝗮𝘆𝗲𝗿 to unify structured and unstructured data, making hybrid and multi-cloud data accessible. ⭘ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗥𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝗰𝘆 ✓ 𝗗𝗮𝘁𝗮 𝗦𝘆𝗻𝗰𝗵𝗿𝗼𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 – Ensures 𝗺𝘂𝗹𝘁𝗶-𝗿𝗲𝗴𝗶𝗼𝗻 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆, keeping operational databases, warehouses, and apps up to date. ✓ 𝗗𝗮𝘁𝗮 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 – Full or partial copies to enhance availability and disaster recovery. ⭘ 𝗢𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 & 𝗔𝗣𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 ✓ 𝗥𝗲𝗾𝘂𝗲𝘀𝘁/𝗥𝗲𝗽𝗹𝘆 – Powers 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 for API-driven architectures and low-latency applications. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? If you’re still relying on 𝗺𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝗶𝗰 𝗘𝗧𝗟 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 for modern data platforms, you’re already behind. The best team architect 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗮𝗶𝗹𝗼𝗿𝗲𝗱 𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗱𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺—that’s how you build a scalable, high-performance system. What’s the biggest integration challenge you’ve faced? Drop a comment. Know someone who’s still struggling with legacy pipelines? 𝗦𝗵𝗮𝗿𝗲 𝘁𝗵𝗶𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝗺.

What are the Top 10 characteristics of a great #ETL, #ELT solution in a Modern Data Stack? 🤚 There are dozens of ELT tools out there. But not all are created equal. 🤔 Here are my thoughts on the criteria you should consider when picking one... 1️⃣ Separate the "EL" from the "T" Doing EL-T instead of ET-L allows you to decouple the process of extracting data and processing the data. Therefore: ♦ the same data can be extracted only once and used for multiple use cases downstream. ♦ these two aspects can be scaled separately. 👉 The EL pipeline should be source-centered 👉 The T (transformation) should be business-centered and kept separate. This distinction between the contexts and pipelines simplifies testing and validation. 2️⃣ Code-first: pipelines need to be version controlled, unit-tested, containerized, and deployed continuously. Develop and test locally, then deploy in production . Stay away from GUI only/first tools! ➡ Follow DevOps automation principles instead. 3️⃣ Modular: reusable loaders and extractors will ensure that you can build new pipeline faster and cheaper. 4️⃣ Schema enforcement: this is important because when schema changes at the source, it will break the downstream processing. 5️⃣ Batch and Realtime support:  Event-driven architecture support moving data in near real-time even from legacy systems (using CDC). 6️⃣ Debuggable: data integration is complex and you will encounter issues. Pick a tool that allows you to troubleshoot easily. 7️⃣ Extensible: easy to add a custom connection, loader or extractor. 8️⃣ Connectors: it is important to have an existing library of connectors for: Files, APIs, CDC (change data capture), Enterprise apps: SAP, Salesforce, Workday, Oracle, and more... 9️⃣ Scalable: As data volumes and the number of data pipelines grow, many data integration tools struggle to handle the scale of the data. You want linear scalability. 🔟 Observable: Logging & monitoring for every step ♦ Ability to capture and raise alerts based on specific rules (ie schema change) ♦ Ability to hook into any alerting system (Slack, Teams, ...) ✅ Bonus: Reverse ETL - this is about integrating analytics back into operational systems, pushing insights in realtime to business applications. #opensource tools that check all these boxes is Meltano for "EL" and #dbt for "T". Check how you can try it below & as usual, let me know your thoughts! 👇

Revolutionizing Data Integration: ETL, ELT, and Reverse ETL in the AI Era In today's data-driven world, efficient data integration is crucial for businesses to gain insights and make informed decisions. Let's dive into the evolution of data integration techniques and how AI is reshaping the landscape. ETL: The Traditional Powerhouse Extract, Transform, Load (ETL) has been the go-to process for decades. It involves: 1. Extracting data from various sources 2. Transforming it to fit operational needs 3. Loading it into the target system (usually a data warehouse) Enter ELT: Flipping the Script Extract, Load, Transform (ELT) emerged with the rise of cloud computing and big data. The key difference: - Data is loaded into the target system before transformation - Leverages the power of modern data warehouses for transformation - Offers more flexibility and scalability Reverse ETL: Closing the Loop A newer player in the field, Reverse ETL: - Moves processed data from warehouses back into operational systems - Enables data activation, turning insights into action - Bridges the gap between analytics and operations AI: The Game Changer Artificial Intelligence is revolutionizing data integration: - Automating data mapping and transformation rules - Identifying data quality issues and anomalies - Optimizing data pipelines for performance - Providing predictive maintenance for data workflows Tools of the Trade Open Source: - Apache NiFi - Talend Open Studio - Airbyte Proprietary: - Informatica PowerCenter - IBM DataStage - Fivetran As data volumes grow and complexity increases, mastering these techniques and leveraging AI will be key to staying competitive. What's your take on the future of data integration?

As customer expectations change, we need to evolve our technical capabilities. The need for real-time data integration is here. IBM recently acquired StreamSets to provide financial services companies a path to realize consistent access and delivery of data across multiple data sources and formats while facilitating the design of smart data pipelines. Why is this important? Here are a few reasons: ✦ 87% of organizations require data to be ingested and analyzed within one day or faster ✦ 82% are making decisions based on state information ✦ 85% state stale data is leading to incorrect decisions and lost revenue With data continuously integrated as it becomes available, streaming data pipelines provide fresh data for various use cases in a time-sensitive manner, such as: ✦ Enhanced customer experiences, with real-time data ✦ Intelligent data pipelines, to reduce data drift ✦ Fraud detection, enabling swift responses to suspicious activities ✦ Real-time reporting and analytics, for immediate actionable insights ✦ Predictive maintenance, with real-time sensor data ✦ Cybersecurity, for enhanced situational awareness This capability is not just impressive, it's a game-changer. It not only addresses current data challenges but also paves the way for managing smart streaming data pipelines to deliver high-quality data needed to drive digital transformation. As Luv Aggarwal explains in his video (https://lnkd.in/e7WEiXfD), by having real-time data pipelines, companies can benefit from continuous, real-time processing, integration, and transfer of data when it is available, reducing latency and data staleness. This provides for better customer experiences and improved insights for agents, partners, and employees when making sales and servicing decisions, as listed in the use cases above. Data is not just a driving force behind innovation and growth, it's the fuel. As described in the IBM Technology Atlas (https://lnkd.in/eQMHn6Dy), data integration is expected to increase in sophistication every year. Real-time data pipelines provide capabilities that enable growth and innovation to realize success. Learn more: https://lnkd.in/eq62r5dk Dima Spivak Scott Brokaw IBM Data, AI & Automation #ibm #ibmtechnology #datapipeline