# data-engineering Data engineering is a discipline within computer science and software engineering concerned with designing, building, and operating systems that collect, store, transform, and deliver data for downstream use. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Future of Data Engineering in the Age of AI | Erfan Hesami DevFeed: [The Future of Data Engineering in the Age of AI | Erfan Hesami]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-16T13:21:19Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [AI Engineering](), [agent observability](), [coding](), [Security]() Tags: [agents](), [ai](), [ai-agents](), [ai-engineering](), [coding](), [data-engineering](), [fundamentals](), [governance](), [security]() ### AI overview An interview with Erfan Hesami examines how AI and agents may change data engineering, including the evolving role of data engineers, the overlap with AI engineering, the continuing importance of fundamentals, and the need to manage governance, security, costs, technical debt, and human judgment. ### Source excerpt AI Agents, Coding & Fundamentals ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## Shehab Amin on Spark Compatibility, Rust, and LakeSail DevFeed: [Shehab Amin on Spark Compatibility, Rust, and LakeSail]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-02T12:22:23Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Apache Spark](), [Rust](), [data-engineering](), [apache-arrow](), [agentic-coding]() Tags: [apache-arrow](), [apache-spark](), [data-engineering](), [rust]() ### AI overview A podcast conversation with LakeSail co-founder and CEO Shehab Amin about Spark compatibility, Rust, Apache Arrow, DataFusion, and data infrastructure. It also discusses data-stack choices, streaming and batch processing, agentic coding, and using existing data pipelines as a basis for AI pipelines. ### Source excerpt Data Engineering Central Podcast. ## Data Engineering Weekly #285 DevFeed: [Data Engineering Weekly #285]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-31T02:51:19Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Data Quality](), [observability](), [Artificial Intelligence]() Tags: [agentic-ai](), [automated](), [data-engineering](), [newsletter](), [observability](), [privacy](), [python](), [quality](), [recovery](), [weekly]() ### AI overview Data Engineering Weekly #285 covers building data platforms, AI chip architectures, preparing data for agentic AI, post-AI data stacks, data modernization, automated data contract breach handling, and privacy-preserving measurement tools. ### Source excerpt The Weekly Data Engineering Newsletter ## Planetary prediction engine: Automating global models via Earth AI DevFeed: [Planetary prediction engine: Automating global models via Earth AI]() Original publisher: [Read original article]() Published: 2026-08-27T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [data-engineering](), [Google](), [Machine learning](), [AI Development](), [Feature Engineering](), [Model Development]() Tags: [agents](), [ai](), [analytics](), [data-engineering](), [earth-ai](), [evaluation](), [feature-engineering](), [generative-ai](), [google](), [insights](), [machine-intelligence](), [mapping](), [research](), [training](), [validation](), [workflow](), [workflows]() ### AI overview Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes. ### Source excerpt Earth AI ## Data Engineering Weekly #283 DevFeed: [Data Engineering Weekly #283]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-17T02:59:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Data Quality](), [Microservice](), [Apache Spark](), [Kafka](), [Feature Engineering](), [audit trail](), [Protocol (disambiguation)]() Tags: [ai](), [anthropic](), [article](), [data-engineering](), [data-quality](), [engineering](), [feature-engineering](), [kafka](), [newsletter](), [pipelines](), [services](), [testing]() ### AI overview Data Engineering Weekly #283 is a newsletter covering data platform fundamentals, multiagent system coordination, payments platform data contracts, financial data quality, declarative data engineering, and cost-efficient export workloads. ### Source excerpt The Weekly Data Engineering Newsletter ## Quasi-Agentic Pipelines with Databricks and Apache Airflow DevFeed: [Quasi-Agentic Pipelines with Databricks and Apache Airflow]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-08-10T21:23:57Z Content type: tutorial Language: en Sources: [Data Engineering Central]() Topics: [databricks](), [airflow](), [Data pipelines](), [Large Language Model](), [data-engineering]() Tags: [airflow](), [apache-airflow](), [data-engineering](), [databricks](), [llms](), [pipelines]() ### AI overview A practical developer discussion of incorporating LLMs and agents into existing data workflows using Databricks and Apache Airflow. It also examines determinism in data pipelines and the gap between business requirements and engineering implementation. ### Source excerpt the strange space in between ## Data Engineering Weekly #282 DevFeed: [Data Engineering Weekly #282]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-10T01:21:26Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [data-platforms](), [data observability](), [Graphs](), [semantic-layer](), [Large Language Model](), [Apache Iceberg](), [gRPC](), [Concurrency](), [Caching](), [Optimization](), [Streaming]() Tags: [caching](), [chaos](), [concurrency](), [data](), [data-engineering](), [data-platforms](), [data-quality](), [knowledge-graphs](), [llms](), [newsletter](), [observability](), [pipelines](), [semantic-layer](), [streaming]() ### AI overview Data Engineering Weekly #282 is a newsletter covering data platform fundamentals, semantic layers, ontology-backed knowledge graphs, converged databases, AI modernization, and Netflix's real-time distributed graph query architecture. It highlights composable architectures, data quality and observability, evolving schemas supported by LLM-assisted extraction, Iceberg full-text search, and optimization techniques including concurrency control, streaming filters, and caching. ### Source excerpt The Weekly Data Engineering Newsletter ## Data Engineering Weekly #281 DevFeed: [Data Engineering Weekly #281]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-03T12:34:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Loop Engineering](), [Artificial Intelligence](), [Large Language Model](), [post-training]() Tags: [agent-harness](), [ai](), [data-engineering](), [feature-engineering](), [genai](), [netflix](), [newsletter](), [post-training](), [weekly]() ### AI overview Data Engineering Weekly #281 covers building data platforms, emerging approaches to AI workflow architecture, data modernization, Netflix's GenRec recommendation system, AI infrastructure modernization, and evaluation practices for generative AI at scale. ### Source excerpt The Weekly Data Engineering Newsletter ## Data Engineering Weekly #280 DevFeed: [Data Engineering Weekly #280]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-27T03:37:19Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Data Infrastructure](), [Open Source](), [Data Quality](), [DuckDB](), [Database]() Tags: [data](), [data-engineering](), [data-infrastructure](), [data-quality](), [database](), [duckdb](), [engineering](), [open-source](), [python]() ### AI overview Data Engineering Weekly #280 is a newsletter roundup covering updates to leetdata.ai and aidataengineer.io, agent-oriented data infrastructure, open-source modern data stack tools, data-tool landscapes, metric certification, and data quality in the AI era. ### Source excerpt The Weekly Data Engineering Newsletter ## ClickHouse achieves AWS Small and Medium Business Competency DevFeed: [ClickHouse achieves AWS Small and Medium Business Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2026-07-23T18:54:44Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services](), [real-time](), [PostgreSQL](), [Software as a service](), [data-engineering](), [Cloud]() Tags: [analytics](), [aws](), [aws-marketplace](), [clickhouse](), [partners](), [postgresql](), [pricing](), [real-time](), [saas](), [security]() ### AI overview ClickHouse has achieved the AWS Small and Medium Business Competency, recognizing its validated expertise and customer success in delivering real-time analytics to small and medium businesses. The article describes ClickHouse Cloud as a fully managed analytics service with PostgreSQL change-data-capture replication, separated compute and storage, pay-as-you-go pricing, and AWS Marketplace procurement. ### Source excerpt ClickHouse has achieved the AWS Small and Medium Business Competency, joining a select group of AWS Partners recognized for deep expertise in real-time analytics for small and medium businesses. ## The CI/CD moment for data analytics DevFeed: [The CI/CD moment for data analytics]() Original publisher: [Read original article]() Author: Gaurav Nanda Published: 2026-07-23T05:40:01Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [data analytics](), [DataOps](), [real-time](), [data-engineering](), [CI/CD](), [Kafka](), [apache-flink]() Tags: [ai](), [analytics](), [apache-flink](), [architectures](), [bridging](), [cost](), [data-analytics](), [data-engineering](), [engineering](), [event](), [fraud](), [google](), [insights](), [kafka](), [latency](), [real-time](), [scale](), [streaming](), [systems](), [tools]() ### AI overview The article argues that data analytics is approaching a CI/CD-like transition toward continuous analytics. It describes how multi-step pipelines introduce latency, operational risk, and maintenance burden, and explains why real-time insight is becoming a baseline platform capability for applications such as personalization, fraud detection, reliability, and AI-driven features. ### Source excerpt How converging OLTP and OLAP architectures are driving 'Continuous Analytics', the CI/CD moment for data to deliver real-time, unified insights and operational simplicity. ## Agentic Data Engineering Is Here -- But Can It Close the Loop? DevFeed: [Agentic Data Engineering Is Here -- But Can It Close the Loop?]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-22T14:05:27Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [data-engineering](), [Orchestration](), [data observability](), [Artificial Intelligence](), [AI-assisted coding](), [DuckDB](), [Apache Iceberg]() Tags: [agentic](), [ai-agents](), [data-engineering](), [duckdb](), [orchestration](), [pipelines]() ### AI overview A podcast conversation with Hugo Lu about agentic data engineering and the infrastructure needed for data platforms to execute work, observe outcomes, validate changes, and improve pipelines safely. It examines why production data systems remain difficult for AI, including schema changes, realistic testing, business semantics, and secure execution. ### Source excerpt a conversation with Hugo Lu ## Data Engineering Weekly #279 DevFeed: [Data Engineering Weekly #279]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-20T04:07:20Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Data Quality](), [semantic-layer](), [Kafka](), [observability](), [knowledge-engineering](), [Embeddings](), [Databases](), [Amazon EKS](), [Amazon EC2](), [Availability]() Tags: [availability](), [data-engineering](), [data-quality](), [database](), [embeddings](), [engineering](), [kafka](), [knowledge-engineering](), [observability](), [semantic-layer]() ### AI overview Data Engineering Weekly #279 is a newsletter covering data platform fundamentals, data management for generative AI, semantic-layer portability, knowledge-base construction with Postgres and embeddings, Kafka migration, and Apache Pinot high availability. The supplied text includes sponsored material and ends mid-item. ### Source excerpt The Weekly Data Engineering Newsletter ## Data Engineering Weekly #278 DevFeed: [Data Engineering Weekly #278]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-13T02:44:42Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Apache Spark](), [Kafka](), [Data Quality]() Tags: [agents](), [ai](), [data-engineering](), [data-pipeline](), [data-quality](), [engineering](), [kafka](), [releases](), [spark]() ### AI overview Data Engineering Weekly #278 is a curated newsletter covering AI-assisted engineering workflows, new leetdata.ai features, agent-oriented data systems, multilingual AI concerns, visualization with Flint, and data platform fundamentals. ### Source excerpt The Weekly Data Engineering Newsletter ## The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering DevFeed: [The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-08T12:16:09Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [pandas](), [data-engineering](), [Artificial Intelligence](), [DuckDB](), [data-platforms](), [software-development](), [future of software](), [Apache Spark](), [Hadoop](), [Open Source](), [parquet]() Tags: [ai](), [apache-arrow](), [arrow](), [data](), [data-engineering](), [data-platforms](), [duckdb](), [hadoop](), [large-language-models](), [open-source](), [pandas](), [parquet](), [software-development]() ### AI overview An interview with Wes McKinney covers the origins of pandas and Apache Arrow, the evolution of modern data engineering from Hadoop to lakehouse architectures, and the roles of tools such as Parquet, DuckDB, DataFusion, and Spark. McKinney also discusses how AI affects software development, arguing that it can improve experienced engineers' productivity but does not replace software engineering, architecture, or judgment. ### Source excerpt interview with Wes McKinney ## Data Engineering Weekly #277 DevFeed: [Data Engineering Weekly #277]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-07-06T04:58:46Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [databricks](), [Database](), [Apache Iceberg](), [Concurrency]() Tags: [concurrency](), [data](), [data-engineering](), [database](), [databricks](), [engineering](), [newsletter](), [protocol](), [replication]() ### AI overview Data Engineering Weekly #277 covers Dagster's internal AI-assisted engineering workflows, the launch of aidataengineer.io and leetdata.ai, and discussions of Databricks LTAP. It also highlights architectural questions around concurrency, copy-on-write, replication, and unified data. ### Source excerpt The Weekly Data Engineering Newsletter ## Data Engineering Weekly #276 DevFeed: [Data Engineering Weekly #276]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-06-29T03:52:17Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [schema-evolution](), [data-architecture](), [Amazon OpenSearch Service](), [Resilience](), [data observability]() Tags: [architecture](), [compatibility](), [data-engineering](), [engineering](), [observability](), [opensearch](), [resilience](), [schema-evolution]() ### AI overview Data Engineering Weekly #276 is a newsletter roundup covering data platform fundamentals, storage and workload architecture, schema evolution in Pinterest's ingestion framework, zone-failure-resilient OpenSearch at Uber, AI modernization, and stateful reasoning systems for notebooks. ### Source excerpt The Weekly Data Engineering Newsletter ## You Don't Graduate From Data Engineering: Why We Built aide for Continuous Learning DevFeed: [You Don't Graduate From Data Engineering: Why We Built aide for Continuous Learning]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-06-23T03:47:56Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Learning](), [Artificial Intelligence]() Tags: [ai](), [career](), [data-engineering](), [learning](), [platform]() ### AI overview The article introduces aidataengineer.io, an AI-powered learning platform built on 275+ editions of Data Engineering Weekly. It argues that bootcamps help people enter data engineering but cannot provide the continuous learning needed as tools, architectures, and practices evolve. ### Source excerpt Introducing aidataengineer.io -- an AI-powered learning platform built on 275+ editions of Data Engineering Weekly. ## Data Engineering Weekly #275 DevFeed: [Data Engineering Weekly #275]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-06-22T04:02:10Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [semantic-layer](), [Data Quality](), [observability](), [Model Context Protocol](), [Python](), [SQL](), [YAML]() Tags: [data-engineering](), [data-quality](), [mcp](), [metrics](), [observability](), [pipelines](), [python](), [semantic-layer](), [sql](), [yaml]() ### AI overview Data Engineering Weekly #275 is a newsletter issue covering data platform fundamentals, semantic layers, metric governance, idempotent pipeline design, and AI modernization. It highlights how shared business definitions, data quality, observability, and retry-safe writes support reliable analytics and AI workflows. ### Source excerpt The Weekly Data Engineering Newsletter ## Beyond the warehouse: How METRO Markets built a do-it-all data platform on ClickHouse Cloud DevFeed: [Beyond the warehouse: How METRO Markets built a do-it-all data platform on ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-18T20:31:40Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [data](), [data-engineering](), [observability](), [real-time](), [Hadoop](), [log management](), [BigQuery](), [Artificial Intelligence]() Tags: [analytics](), [bigquery](), [clickhouse](), [cloud](), [data](), [data-engineering](), [hadoop](), [infrastructure](), [logging](), [observability](), [real-time]() ### AI overview METRO Markets replaced its self-hosted Hadoop-based data stack with ClickHouse Cloud, consolidating company data into a single warehouse that supports data warehousing, real-time seller analytics, credit risk modeling, observability, operational logging, and new AI use cases. ### Source excerpt How METRO Markets replaced a failing Hadoop-based stack with ClickHouse Cloud to build a single platform now powering data warehousing, real-time seller analytics, credit risk modeling, observability, and AI across the whole company. ## Data Engineering Weekly #274 DevFeed: [Data Engineering Weekly #274]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-06-15T05:29:03Z Content type: news Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Data Quality](), [anthropic](), [data-architecture](), [AI, ML & Data Engineering](), [Claude](), [Messaging]() Tags: [anthropic](), [architecture](), [claude](), [data](), [data-architecture](), [data-engineering](), [data-quality](), [engineering](), [software]() ### AI overview Data Engineering Weekly #274 covers data platform fundamentals, Anthropic's use of Claude for self-service analytics, Airbnb's data architecture and ownership conventions, Uber's data abstraction layer, and semantic search for AI agents. ### Source excerpt The Weekly Data Engineering Newsletter ## In 2026 The Data Fundamentals Matter More Than Ever DevFeed: [In 2026 The Data Fundamentals Matter More Than Ever]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-06-13T22:51:35Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data-engineering](), [data-modeling](), [Data Science](), [Python](), [SQL](), [Artificial Intelligence]() Tags: [2026](), [data](), [data-engineering](), [data-science](), [developer](), [fundamentals](), [python](), [sql]() ### AI overview This opinion article argues that data fundamentals remain important in 2026 despite changing technology trends and job titles. It identifies messy data and weak data foundations as persistent bottlenecks, and emphasizes SQL, Python, data modeling, and related engineering skills. ### Source excerpt Otherwise we are headed towards a massive data mess [Next page]()