In this post I will setup the next post by reminding you of these numbers every programmer should know. The picture shows the latency to access data across the three levels of cache in a modern processor, and across the memory bus to DRAM, and then across to an SSD or spinning disk drive. These numbers are … Continue reading “Numbers Every Database Professional Should Know”
This post describe the model used to generate the content from the DW Cloud Economics – A Do Over post incase you want to check the numbers… First, keep in mind that the numbers in this model are relative and indicative, not absolute. Here are the fixed parameters I used to generate these indicative numbers: … Continue reading “DW Cloud Economics – The Model”
Over the next several weeks, I’ll share my perspective of current best practices for big data, which is the term I’ll use to blend thinking about analytic data systems: data lakes, data warehouses, data marts operational data stores. On this journey, I’ll consider how analytic workloads are changing with AI and machine learning, discuss data … Continue reading “Cloud-native Computing, Workloads, and Elasticity”
The last post (here) demonstrated how scalability in the cloud provides the ability to reduce runtimes from days or hours to minutes without raising the cost. We used a batch ETL service running three ETL scripts as an example. We then showed how the same use of scalability could allow us to break the batch … Continue reading “More on Cloud Data Elasticity”
Today I am going to focus on a topic that I’ve suggested previously without the right emphasis: the new database architecture that uses vector processing on compressed columns to significantly accelerate performance. The term “super-computing” was coined to describe the extreme hardware and software optimization developed to crunch numbers in scientific applications. As these technologies developed … Continue reading “Database Super-computing”
Last year two associates from Greenplum suggested that I read a very smart academic paper titled “Efficiently Compiling Efficient Query Plans for Modern Hardware” by Thomas Neumann. Having reiterated the idea of database supercomputing in my last blog (here)… I can now suggest this paper to you. In short this paper suggests that the classic … Continue reading “More Database Supercomputing Technology”
Back to more geeky topics… although my Mom loved the videos… When very high performance is required to return key performance metrics derived from large volumes of data to a very large number of clients… in other words when volume and velocity are factors and the results are to be delivered to thousands of users (I … Continue reading “Key Values and Key-Value Stores and In-memory Databases”
Yesterday I provided a model for how business sees open source as a means to be profitable (here). This is the game Pivotal seems to be playing with their release of Hadoop, Gemfire, HAWQ, and Greenplum into open source. I do not know their real numbers… so they may need more or fewer additional customers than … Continue reading “Thinking About the Pivotal Announcements…”
This post is more about the technology business than about technology… but it may be relevant as you try to sort out winners and losers… and this sort of sorting is important if you consider new companies who may, or may not, succeed in the long run. To make my point let us do a … Continue reading “Open Source is Not a Market…”
Database vendors don’t usually admit to shortcomings… they protest that they have no shortcomings until the market suggests otherwise… then they make some sort of change that signals an admission. This post will explore three of these admissions: Oracle and the shared-nothing architecture, DB2 on the mainframe and the shared-nothing architecture, and Teradata and in-memory … Continue reading “How DBMS Vendors Admit to an Architectural Limitation: Part 1 – Oracle Exadata”
