Notes from the compute layer

Apache Iceberg, Delta Lake, and Apache Hudi solve similar lakehouse problems but suit different workloads. Iceberg is the strongest choice for open.

This blog shows how e6data decomposes metric anomalies into structural, causal, and narrative layers and feeds the output into an AI agent that can.

The transition to open table formats like Apache Iceberg and Delta Lake solved storage vendor lock-in. It also created a massive financial bottleneck at.

Learn proven data warehouse cost optimization strategies for Snowflake, BigQuery, Redshift, and more. This 2026 guide covers pricing models, cost drivers.

This post explains a subtle async Rust bug where tokio::sync::Mutex appears to deadlock despite logs showing the lock was released. The real issue is a.

Apache Iceberg can OOM during planning when the file count inflates the metadata to terabytes. Fix it by streaming metadata reads (bounded batches).

The Variant data type stores semi-structured JSON in a binary, self-describing format with a per-row key dictionary (“metadata”) and compact values..

e6data’s blog explains how German Strings improve string performance in real-time analytics by replacing pointer-heavy strings with a compact 16-byte.

Partition projection replaces manual, Hive-style partition registration with rule-based metadata that query engines compute on the fly. You keep the.

Organizations are increasingly adopting lakehouse architectures to unify data warehouses and data lakes in today’s data-driven world. The Hive Metastore.

Apache Iceberg is an open table format designed for managing petabyte-scale analytic datasets, providing a high-performance solution for modern data.

Data analytics, as we know it, is undergoing a seismic shift. SQL query engines traditionally designed for batch workloads on Lakehouse architectures like.

This is a guide to building a lakehouse pipeline in Snowflake: Snowpipe auto‑ingests JSON orders to a landing table; a stored procedure cleans and.

The structural shift from monolithic, proprietary data warehouses toward modular, open data lakehouse architectures is a financial and operational.

How to implement detection of subgraphs within a query plan | Demonstrate pruning and cost-based decisions | Redundant computations | Automatic CTE

This blog compares the top embedded analytics tools for B2B SaaS companies in 2026, covering platforms such as e6data, Looker, Tableau, Power BI, Sigma.

Run semantic search in SQL with cosine similarity and without a separate vector DB. e6data’s LOAD VECTOR and Blitz handle on-the-fly embeddings, quantized.

Apache Iceberg is a high-performance, open-table format designed to manage large-scale analytic datasets with reliability and efficiency. It enables.

In the traditional world of data warehousing, metadata catalogs quietly operated behind the scenes. They were simple data dictionaries within a monolithic.

The emerging Iceberg catalogs are reshaping metadata management strategies for 2025. From Git-style branching in Nessie to the lean Rust performance of.

Connect once, query everywhere. Our latest release provides advanced support for Apache Iceberg, Apache Polaris, Apache Hudi, and Delta Lake, enabling you.

Traditional multi-statement SQL User-Defined Functions give developers modular, expressive power but at the cost of brutal row-by-row execution and.

Apache Iceberg is a cornerstone table format in modern data lakehouse systems. It is renowned for its ability to deliver transactional consistency, schema.

Choosing the correct compression codec is crucial when optimizing Apache Iceberg tables for fast writes and reads. Iceberg often ends up writing many.

Geospatial performance bottlenecks appear the instant a lat/lon B-tree creeps past a hundred million rows. This guide benchmarks H3 hex grids against.

Distributed stream processing fundamentally involves choosing between stateful stream processing —used extensively in event-time-based analytics and.

Lakehouse, or what is often called an open table format like Apache Iceberg, is a specification that helps achieve two key things: 1. Representing a table.

Introduction Efficiently processing large datasets is essential and challenging in big data and distributed computing. At E6data, we've encountered many.

At e6data, we are big admirers of Apache Iceberg. We're witnessing a steep increase in its adoption, with our customers running E6data's query engine for.

The shift from disparate data warehouses and data lakes to the lakehouse architecture is a structural response to the scaling limits of analytical.

In traditional data warehouses, storage and computing are tightly integrated. By contrast, lakehouses–built with the Apache Iceberg format and Delta.

Vector and semantic search let lakehouse teams unlock 80% of enterprise data that lives in free-form text, images, and other unstructured formats. By.

e6data brings SQL and AI together, letting you query unstructured data directly in Microsoft Fabric 10x faster and 60% lower cost—no pipelines, no.

e6data , a lakehouse compute engine, today announced the successful completion of key enterprise compliance certifications, including SOC 2, HIPAA, GDPR.

In today’s fast-paced world of data management, every organization is on the lookout for innovative ways to efficiently store, manage, and analyze large.

e6data integrates with Microsoft Fabric to make querying data about 33% faster (as per benchmark reports) — helping you speed up data analytics while.

e6data to level the playing field for customers by negating the immense pricing power a handful of vendors enjoy due to various new forms of compute.

Query your existing OneLake data about 33% faster, cutting analytics costs by 2-3x without changing your Fabric workflow.

e6data's founding engineer and Head of Engineering, Sudarshan Lakshminarasimhan, unpacks the internals of their compute engine on Zero Prime podcast

e6data’s hybrid lakehouse keeps compute beside the data across AWS, Azure, GCP, and on-prem, wiping out cross-region egress charges and reducing query.

This article explains why enterprises must own the context layer for AI that gives AI agents reliable business meaning. It shows how an enterprise context.