Recently I have had the impression that a new data architecture is forming. My instinct says that is wrong, because we already have more architectures than most organisations know what to do with, and adding another one to the pile is nobody’s idea of progress. But the signals keep arriving: Machine-generated data has stopped being
Demystifying Why Data Architectures Change Read More »
Most database shops have a caching layer. Redis, Memcached, something custom. Reads hit the cache, misses hit the database, and someone owns the invalidation logic, the TTLs, and the 2 AM incident when the cache goes stale. Oracle 26ai introduces True Cache, and it works nothing like Redis. It’s a diskless, read-only database replica that
True Cache on Oracle 26ai Read More »
In the AI data infrastructure race, Oracle wants you to run AI inside your database. Not call an API. Not ship your data to an external LLM company. Not stand up a separate vector database. Run the whole thing on-prem, behind your firewall, managed by your data team. That’s what the Private AI Services Container
Running Oracle’s Private AI Stack On-Premises Read More »
Everyone is talking about AI. AI this, vector that, vector indexes eating your storage, running out of memory. I deployed 26ai on my lab cluster and went through the internals so you don’t have to guess what’s actually going on under the hood. Brace yourself for this DDL and DML ride. Here’s the short version
Oracle 26ai Vector Search Internals Read More »
Oracle 26ai came out. I deployed it, went to run my RMAN workflows, and immediately noticed things had changed. After an hour of reading through the 26ai Backup and Recovery documentation and testing against a live instance I found enough worth writing down. This post is part change log, part lab journal for what’s different
What Changed in RMAN for Oracle 26ai Read More »
Oracle 26ai can join external tables backed by S3 Parquet files with regular internal tables in a single SQL statement, without data movement, ETL pipelines, or Spark. The optimizer treats S3-backed external tables as another data source and picks the right join strategy automatically. This post shows how to build a customer dimension table from
Hybrid Analytics with Oracle 26ai and S3 Data Read More »
I spent months building a tool that answers a question nobody else was asking: how many databases can your infrastructure actually handle before something breaks? HammerDB-Scale orchestrates parallel database benchmarks on Kubernetes, correlates application metrics with storage behaviour, and finds consolidation limits. I also overengineered it for the wrong user experience. But I thought it
HammerDB-Scale 2.0 and Why I Rebuilt the Whole Thing Read More »
TL;DR: DBMS_CLOUD.CREATE_EXTERNAL_TABLE creates an external table that reads Parquet directly from S3. Define the table once, query it with standard SQL. No data loading, no ETL. With S3 connectivity working (Part 1 and Part 2), this post shows how to create DBMS_CLOUD external tables over Parquet files in S3 and query them from Oracle 26ai.
Querying Parquet Files on S3 with DBMS_CLOUD in Oracle 26ai Read More »
TL;DR: DBMS_CLOUD fails with non-AWS S3 because it parses hostnames to select the signing algorithm and extract the AWS region. Fix: point s3.us-east-1.amazonaws.com at your endpoint via DNS, add it as a SAN on the endpoint’s TLS cert, and import the cert into an Oracle wallet. Full DBMS_CLOUD functionality, no file downloads. In Part 1,
Solving DBMS_CLOUD Connectivity with S3-Compatible Storage Read More »
TL;DR: DBMS_CLOUD requires valid HTTPS certificates with matching hostnames. Self-signed certs or HTTP-only? DBMS_CLOUD won’t work. This post shows what breaks and why. Version Note: This uses Oracle 26ai (23.26.1.0.0). SSL handling behavior changed in 23ai. On 19c/21c, sqlnet.ora settings like SSL_SERVER_DN_MATCH=NO still work, so self-signed certs may be viable with additional configuration. The steps
Connecting Oracle 26ai to S3-Compatible Object Storage Read More »
Finding which lakehouse architecture performs best on your infrastructure is expensive. Deploying a single stack on Kubernetes by hand takes days (ask me how I know). A different catalog, a different query engine, a different version, different resource profiles. The cost of the first deployment is high. The cost of repeating it for every permutation
Deploy, Score, and Compare Data Infrastructure on K8s with Lakebench Read More »
Part 2 of The Economics of Data Architectures in 2026 Part 1 surveyed seven architecture patterns, each with its own economics. Warehouse bets on centralization. Lakehouse bets on eliminating the ETL tax. Mesh bets on distributed ownership. And so on. But here’s the question nobody asks at selection time: what will it cost to leave?
Data Architectures and Change Management Read More »
Part 1 of The Economics of Data Architectures in 2026 The Modern Data Stack is Dead The Modern Data Stack (MDS) was never an architecture. It was a philosophy: best-of-breed tools, loosely coupled via APIs, separation of concerns. Fivetran for ingestion. Snowflake for storage. dbt for transformation. Looker for visualization. Buy the pieces, assemble yourself.
Data Architectures and Their Economics Read More »
I installed Oracle RAC 19c on a two-node cluster with Pure Storage FlashArray backend. Four 32Gb Fibre Channel HBAs per node. 24 paths per LUN. Hardware capable of 12+ GB/s throughput. Yet my benchmarks topped out at 3.2 GB/s. This post explains the mistake I made, how I found it, and how I fixed it.
How to Avoid Misconfiguring Oracle ASM with Multipath SAN Storage Read More »
I recently built a two-node Oracle RAC 19c cluster on Oracle Linux 8.10 with Pure Storage FlashArray backend. The hardware was solid, the network was configured, the storage was presented. What should have been a straightforward installation turned into a four-hour troubleshooting session because of issues that aren’t documented in Oracle’s installation guides. This post
Installing Oracle Database 19c RAC on OEL 8: Four Traps Read More »
This is the third in my series analyzing data-centric markets. See also: Demystifying the Database Market in 2025 and Demystifying the Data Governance Market in 2025. It’s interesting how major vendors position themselves now, none of them lead with “analytics”. Databricks calls itself “the Data and AI company.” Snowflake is “the AI Data Cloud.” dbt
Demystifying the Analytics Market in 2025 Read More »
Oracle ranks #1 on the DB-Engines database popularity ranking, ahead of MySQL, SQL Server, and PostgreSQL. Running Oracle benchmarks alongside SQL Server in my day-to-day testing made perfect sense from the start, but HammerDB-Scale v1.0 focused on getting SQL Server working properly first. Version 1.1 adds Oracle support with the same parallel orchestration framework that
Oracle Database Support for HammerDB-Scale Read More »
You will end up here: v$dnfs_servers returns no rows. You’ve checked everything. The library is linked. The alert log shows dNFS loaded. Your oranfstab is syntactically perfect. The NFS mounts are there. And you’re convinced it’s broken, I was too. I spent three hours proving dNFS was configured correctly while simultaneously proving it wasn’t working.
Schrödinger’s dNFS: The Empty Row Mystery Read More »
The Economics of Data Gravity In 2010, Dave McCrory introduced the concept of “data gravity” to describe a phenomenon IT professionals were increasingly experiencing: as data accumulates in one location, it becomes harder to move, and the systems that depend on it tend to cluster nearby. The metaphor suggested that data, like physical mass, exerts
The Economics of Data Gravity Read More »
Your storage vendor claims 500K IOPS. Your server vendor promises 128 cores of “enterprise-grade performance.” Your hypervisor can “easily handle” 50 VMs. Your network fabric has “plenty of headroom.” Then you consolidate eight production databases onto the platform and everything falls over at 3 AM on a Tuesday. What happened? Nobody actually tested what happens
Stress Testing Consolidated Platforms with Database Workloads Read More »
This is the second in my series analyzing data centirc markets. Read part one: Demystifying the Database Market in 2025. According to Gartner (2024), by 2027, 80% of data and analytics governance initiatives will fail due to a lack of a real or manufactured crisis. This is a pretty loaded statement, because it tells us
Demystifying the Data Governance Market in 2025 Read More »
This is the unmanaged experience of Apache Spark in 2025, and it is a Screen Play in length, you were warned! P.s. – Netflix greenlight me! A few weeks ago, I dove deep into Apache Spark through hands-on implementation of a Customer 360 use case, using the medallion pipeline processing architecture (Bronze → Silver →
Apache Spark in 2025: Handle with Gloves Read More »
The Database Management System Market in Plain Language Your job, unfortunately, depends on a database doing what it’s supposed to: record the firm’s relationship to money in a structured format. If that database went away the world would absolutely collapse. I wish I was kidding, but think about it: if your corporate ERP system goes
Demystifying the Database Market in 2025 Read More »
I first began working with databases when I was an undergrad doing Computer Science. There was a module that ran tutorials for data manipulation against an Oracle database using SQL. I remember what I was thinking when trying to figure out how to get the correct output : “I don’t understand, why won’t it work!”.
A history of myself, DOES and data Read More »
There are many database benchmarking and testing tools that can be used to understand how a system might behave under specific circumstances. Each one of these has a specialism, some of them are designed for unit testing while others are workload oriented. After several years working towards this goal I am excited to announce my
Simple Database Testing with D.O.E.S Read More »
Recently while doing some bits and pieces around the containerization of various databases I found myself deploying RedHat® OpenShift®. I found in my case there were a number of caveats to make everything work as intended and so I decided to see if a step by step of my experience could be helpful to others.
Deploying an OpenShift Cluster on VMware vSphere Read More »
This blog post is focused on the different datasets I have designed to evaluate Pure Storage® DirectMemory Cache with various databases like SAP HANA, MySQL and MariaDB. More information on the results can be found in the blog posts Reducing costs for SAP HANA with Pure’s DirectMemory Cache and Accelerate MySQL Read Scale with DirectMemory
Evaluating Storage Read Cache for Different Database Designs Read More »
Caching is used in hardware and software to shorten operational response times for data with a high usage frequency. CPU’s implement caching through the use of smaller and faster memory on or close to the processing unit where data can be retrieved and processed more quickly than in main memory. For persistent storage like hard
Introduction to Storage Caching Read More »
MySQL and MariaDB are two very popular database engines (Source – DB-Engines Ranking). Both are open source (licensed as GPLv2 or proprietary for MySQL or LGPL for some scenarios with MariaDB) which allows for abundant support , lower software costs and no vendor lock-in. There are many reasons an organization would want to make use
MySQL and MariaDB Replication Read More »
There are a number of ways in which data protection/data mobility can be done with MySQL. The documentation splits backups into two categories. Physical Backups – Raw copies of directories and files which store database contents Logical backups – Logical backups save information in the form of reproducing the database object structure to the point
MySQL Backup and Recovery Read More »
Last year I gave an agent read access to a warehouse, a schema catalogue, and a brief to explain movements in a set of business-critical numbers. It produced an invoice I had not anticipated. Accuracy was never the difficulty; the model was largely correct. What consumed the budget was interrogation. Before it could attempt anything
The Economics of Agentic AI Analytics Read More »
