SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Federation vs. Lakehouse: Why Smart Data Teams Use Both, Not One

0
·1 views

Modern enterprises struggle with data sprawl across dozens of operational databases, warehouses, lakes, and SaaS platforms, making unified data access a critical priority. Two main architectural approaches exist: data federation, which queries data in place without moving it, and the lakehouse model, which consolidates data into a single governed store using open formats like Apache Iceberg. Each approach has distinct strengths and failure modes — federation hits performance ceilings at scale, while lakehouse migration of legacy sources can take years and significant cost. Experts argue the two are not rivals but complementary phases, with federation enabling fast access and the lakehouse serving as a promoted, governed destination for high-value workloads. The stakes have risen further with AI adoption, as agents and assistants require a single, governed data entry point to function safely and effectively.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

TrulyFreeOCR: Open-Source Java OCR Pipeline Runs Offline With No Native Dependencies

A developer has released TrulyFreeOCR, an open-source OCR pipeline that converts scanned PDFs into searchable, compressed PDFs using a single executable JAR file. The tool is built entirely on Apache 2.0, MIT, and BSD-licensed components, making it suitable for business use without GPL or AGPL restrictions. It requires no system-level installations, as a bootstrap script automatically downloads all dependencies including JDK, Tesseract, and Leptonica into local project directories. The pipeline runs fully offline on CPU hardware and applies MRC compression techniques to achieve 5–10x file size reduction compared to JPEG-only output. The project was created after the author evaluated over 20 existing tools and found none that satisfied all requirements around licensing, deployment simplicity, offline operation, and compression.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

🚀 Calling all DevOps, SRE, and Platform Engineers! Let’s build the future of AI for DevOps together.

Over the last few years, I've been exploring AI agents, and one thing became obvious. Today, the project includes: But this is just the beginning. There is so much more we can build together: ⭐ Give the repository a star Let's build the open-source AI platform that every DevOps engineer wishes existed. https://github.com/ideaweaver-ai/devops-open-agent https://youtu.be/3VT8MeSLt5s

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

How Object Storage Became the Backbone of Fast Modern Analytics

Despite being 50–100 times slower in latency than block storage, object storage like Amazon S3 has become the dominant foundation for the world's analytical data systems over the past decade. Block storage, used in cloud services such as AWS EBS, offers sub-millisecond response times and is essential for transactional databases, but its architecture limits scalability and sharing across machines. Object storage trades raw speed for massive scalability, lower cost, and high durability, making it attractive for large-scale data lakes and lakehouses. Engineers addressed object storage's latency drawbacks through innovations in file formats like Apache Parquet, table formats like Apache Iceberg, and query engines such as Dremio. Together, these technologies systematically neutralized object storage's weaknesses, enabling interactive analytics at scale without reverting to costlier block-based infrastructure.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Audit Uncovers 14 Months of Silenced Infrastructure Alerts Hidden Behind AI Dashboard

During a routine network infrastructure audit, consultant Mark Johnson discovered a muted Slack channel that had been quietly generating undelivered alerts for 14 months. The channel, set up by a former SRE identified only as JL, contained a sophisticated multi-layered alert system covering CPU trends, memory leaks, and connection pool thresholds. When the client upgraded to a new AI monitoring dashboard, the legacy alert channel was muted rather than decommissioned, effectively burying its output. JL left the company shortly after the platform switchover, leaving behind a handover note referencing the silenced channel. The finding raised questions about what critical infrastructure signals had gone unnoticed during the period the new dashboard was in use.

0 comments Read more at DEV Community