Arindam Banerji’s Post

Data Engineering in 2024: Pioneering the Future of Data Quantum Leaps, AI Synergy... As we approach the end of 2024, data engineering evolves rapidly, shaping how organizations leverage their data assets. Here's a concise overview of current trends and future directions: 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 1. Advanced Data Quality and Observability   - 85% of Fortune 500 companies now use AI-driven data quality tools   - "Quality-as-code" practices are becoming standard   - Causal inference techniques are enhancing anomaly detection 2. Microservices and Event-Driven Architectures   - 78% of organizations use event streaming for critical operations   - Data contracts are widely used to manage inter-service dependencies   - Specialized data mesh platforms are emerging 3. Cloud-Native and Multi-Cloud Strategies   - 92% of enterprises employ multi-cloud strategies   - Cloud-agnostic data tools market has grown 200% since 2022   - "Cloud-agnostic data fabrics" provide consistent governance across clouds 𝗖𝘂𝘁𝘁𝗶𝗻𝗴-𝗘𝗱𝗴𝗲 𝗧𝗿𝗲𝗻𝗱𝘀 1. AI-Augmented Data Engineering   - 70% of data engineering tasks are now AI-assisted   - Large language models generate and optimize ETL code   - "AIOps for data" platforms predict and prevent pipeline failures 2. Quantum-Ready Data Infrastructure   - 15% of Fortune 100 companies have initiated quantum-ready projects   - Investment in quantum-resistant encryption has grown 300% since 2022   - Quantum machine learning is being explored for complex data analysis 3. Edge Computing and Real-Time Analytics   - 65% of enterprises process some data at the edge   - "Edge data mesh" architectures enable distributed processing   - 5G and satellite internet facilitate real-time data streaming from remote locations 𝗥𝗲𝗴𝗶𝗼𝗻𝗮𝗹 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝘀 - North America leads in AI-augmented data engineering adoption - Europe shows highest adoption of privacy-enhancing technologies - Asia-Pacific leads in edge computing, especially in manufacturing and smart cities - Latin America sees the fastest cloud adoption growth for data workloads 𝗙𝘂𝘁𝘂𝗿𝗲 𝗢𝘂𝘁𝗹𝗼𝗼𝗸 1. Autonomous Data Ecosystems: Expected by 2026, self-optimizing and self-healing 2. Quantum Data Analytics: Significant advantages in specific domains by 2027 3. Brain-Computer Interfaces: Experimental systems for data interaction by 2028 4. Ethical AI Governance Platforms: Widespread adoption expected by 2025 5. Exascale Data Processing: Available as a service by 2026 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 Data engineering in 2024 spearheads innovation in AI, quantum readiness, edge processing, and ethical data practices. As we approach 2025, the field promises incremental gains and paradigm shifts in data handling. Organizations adept at navigating these trends will lead to our data-driven future. 👋 I'm Siddhartha Vemuganti, Data Engineering & AI/ML leader. Passionate about scalable AI futures. Repost ♻️, Follow & 🔔 for more insights on data, AI, and tech's future!

About 1/3 of GenAI adopters lack the skills or tools to prepare unstructured data for language models. So data engineering teams have some work to do. To put these these survey findings in perspective, check out my new blog, sponsored by Datavolo: "Why and How Data Engineers Will Enable the Next Phase of Generative AI." https://lnkd.in/gX82Jp3N Excerpts below. I'd love to hear stories from practitioners that have solved this problem--and those that still struggle with it. Because GenAI largely consumes unstructured data, data engineers must build new pipelines that effectively process and deliver this type of data. This represents a new challenge because data engineers historically focused on structured tables rather than unstructured documents, images, or video files.  Today unstructured data sloshes through email systems, CRM applications, videoconferencing software, and other parts of the organization. Companies need to consolidate, parse, and prepare this data for GenAI. Here is an example of an unstructured data pipeline that does this with text files.  > Extract First the pipeline parses and extracts relevant text and metadata from applications and files, including complex documents with embedded figures and tables. > Transform Next, the pipeline transforms the extracted documents. It divides the text into semantic “chunks” and creates vectors that describe the meaning and interrelationships of chunks. It also might enrich these document chunks with data from other systems and data platforms. (Some pipeline tools perform these transformation steps in an intermediate landing zone using an ELTL sequence.) > Load Finally, it loads and embeds these vectors into a vector database such as Pinecone and Weaviate or vector-capable platforms such as Databricks and @MongoDB. These vectors are now ready to support GenAI. Data engineers must design, deploy, and monitor these pipelines, and orchestrate how they interact with vector databases and GenAI applications. They also might need to orchestrate how GenAI applications integrate with predictive ML models or other analytical functions as part of larger workflows. In addition, data engineers need to observe both data quality—for example, to ensure no vectors are lost or duplicated—and pipeline performance. Data/AI experts, what do you think? Chime in here. Thank you Luke Roquet and Sam Lachterman for sharing perspectives on the experiences of Datavolo customers in this area. Syed Tanveer Jishan Brian Greene Stephen Rausch Andrea Pisoni Landon Walsh Randolf Reiss Garth Miles Shawn Rogers Debra Peryea Wayne Eckerson Jay Piscioneri Abdul Fahad Noori #dataengineering #generativeai #genai

Kevin Petrie is putting together a series on data engineering for AI Systems, the unique challenges and opportunities it presents, and things to consider on the journey. He is always full of insights and well worth following!

I agree that the ELT/ETL process for RAG is very similar to that of the more "traditional" tabular data. I am really curious as to how we are going to resolve data quality issues in these new types of pipelines. Previously some technical ability could be assumed of data owners/producers, when the sources where for example ERP systems - where generally relatively data-savvy people worked with and produced data. Data Engineers could quite easily converse and resolve issues with these owners. Whereas with unstructured data literally anyone in the organisation can be producing relevant data, also people who might not speak the "language of data". I expect data governance and data literacy training to get a more and more important role as we see GenAI rolled out across organisations. Which will in turn also increase the quality of the more "traditional" tabular data pipelines and products. Win-win if you ask me.

Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02qzZfh0

Data integration refines the fuel that drives the AI/ML model lifecycle, from model development to training to deployment and operation. Here's how. Also check out my latest blog, sponsored by CData Software and published by BARC's partner Eckerson Group: https://lnkd.in/g7uh9f6C Excerpts below. (As for the Porsche graphic, I might have gotten carried away with the analogy that highly refined fuel (i.e., data) drives high-performance cars (i.e., AI/ML). Our 18-year-old son's passions are rubbing off on me 😲) To start, let’s consider how data integration supports the foundational steps of data labeling and feature engineering for predictive ML models. This process also can apply to recommendations and anomaly detection, in particular those that consume structured data. Data scientists, data engineers, and ML engineers start by collecting the historical input data that relates to the business problem at hand. For example, they might integrate their data using a mix of ETL and data virtualization. They extract, transform, and load their operational records into a lake house; and create virtual views of distributed unstructured data that is difficult to extract from heritage systems. The data scientist provides close oversight to ensure both the consolidated dataset and virtual views meet AI/ML model requirements—no easy task for a heterogeneous environment. Next data engineers, ML engineers, and data scientists collaborate with business owners to “label” various outcomes in their historical data sets. This means they add tags to data to easily identify historical outcomes, such as robotic arm failures, fraudulent transactions or the price of recent house sales. They also might label customer emails and social media posts as “positive” or “negative” to create an accurate model for classifying customer sentiment. Data engineers and data scientists need to label outcomes accurately and at a high scale. This requires a programmatic approach, automation, and assistance from business owners that best understand the domain. Note that labeling applies to supervised ML only. Unsupervised ML, by definition, studies input data without known outcomes, which means the data has no labels. Data/AI gurus: does this sound right? Tell us about your use cases, best practices, and lessons learned. Stay tuned for blog excerpt 2, focused on performance of production models. Shawn Rogers Timm Grosser Florian Bigelmaier Joo-Ang "Sue" Raiber Lucia Santamarina Nick Golovin 🇺🇦 Paulina Rios Maya EM360Tech #data #ai #analytics

Great read on some of the basic fundamentals around data engineering for AI Systems. Lots more insights to come from Kevin Petrie on this topic!

Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02qTWrs0

🔍🏗️ Rethinking Your Data Architecture: Powering the Future with Generative AI McKinsey's report in June 2023, 'The Economic Potential of Generative AI: The Next Productivity Frontier,' highlights some compelling facts and predictions about AI: 🔹 Generative AI could add the equivalent of $2.6 trillion to $4.4 trillion in annual economic benefits across 63 use cases. 🔹 Seventy-two percent of leading organizations note that managing data is already one of the top challenges preventing them from scaling AI use cases. Traditional data architectures are not optimized for handling the variety and volume of unstructured data. They struggle to meet the demands of generative AI. For maximum business value from generative AI, companies must develop adaptable and integrated data architectures tailored to these specific needs. In response to these insights, McKinsey proposes some critical actions for leaders to consider when planning data architecture redesign: 1️⃣ Focus on value-centric data. 2️⃣ Incorporate features like vector databases to support a wide range of applications, especially for unstructured data. 3️⃣ Implement multiple, both human and automated, interventions to maintain high data quality. 4️⃣ Secure sensitive data and stay adaptive to evolving regulations. 5️⃣ Prioritize hiring skilled data engineers. 6️⃣ Utilize generative AI to enhance tasks across the data value chain. 7️⃣ Invest in monitoring and swiftly address issues to elevate data performance. 🛠️ Building Capabilities into Architecture to Support GenAI Use-Cases 🤖: To utilize generative AI effectively, data leaders must review and revamp existing architectures, ensuring a robust data foundation and identify necessary upgrades for high-value use cases. Here are five critical components for consideration: 1️⃣ Unstructured Data Stores: Map and tag unstructured data for efficient processing and high-quality, transparent data pipelines. 2️⃣ Data Preprocessing: Convert and clean data, especially sensitive data, for generative AI use; standardize large-scale data handling. 3️⃣ Vector Databases: Prioritize and streamline access to contextual data for effective generative AI queries. 4️⃣ LLM Integrations: Implement frameworks and establish guidelines for integrating Large Language Models with multiple systems. 5️⃣ Prompt Engineering: Structure prompts to optimize AI responses, integrating knowledge graphs and standardizing data input protocols. Curious to learn more about AI and data? Follow me to explore the latest trends, industry insights, and innovative use cases. Boost your generative AI projects with AI-powered web scraping tools. Start now for advanced data extraction! Visit https://lnkd.in/dicsEgqu to begin. #bigdata #webscraping #generativeai #technology

Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02qBt0Y0