Examples of multi-layer, three-tier data-processing architecture.
Like CPU caches, which tend to be arranged in multiple levels, modern organizations direct their data into different data stores under the principle that a small amount is needed for real-time decisions and the rest for long-range business decisions. This article looks at options for data storage, focusing on one that’s particularly appropriate for the “fast data” scenario described in a recent O’Reilly report.
Many organizations deal with data on at least three levels:
- They need data at their fingertips, rather like a reference book you leave on your desk. Organizations use such data for things like determining which ad to display on a web page, what kind of deal to offer a visitor to their website, or what email message to suppress as spam. They store such data in memory, often in key/value stores that allow fast lookups. Flash is a second layer (slower than memory, but much cheaper), as I described in a recent article. John Piekos, vice president of engineering at VoltDB, which makes an in-memory database, says that this type of data storage is used in situations where delays of just 20 or 30 milliseconds mean lost business.
- For business intelligence, theses organizations use a traditional relational database or a more modern “big data” tool such as Hadoop or Spark. Although the use of a relational database for background processing is generally called online analytic processing (OLAP), it is nowhere near as online as the previous data used over a period of just milliseconds for real-time decisions.
- Some data is archived with no immediate use in mind. It can be compressed and perhaps even stored on magnetic tape.
For the new fast data tier, where performance is critical, techniques such as materialized views further improve responsiveness. According to Piekos, materialized views bypass a certain amount of database processing to cut milliseconds off of queries. Read more…
Predixion service could signal a trend for smaller health facilities.
Analytics are expensive and labor intensive; we need them to be routine and ubiquitous. I complained earlier this year that analytics are hard for health care providers to muster because there’s a shortage of analysts and because every data-driven decision takes huge expertise.
Currently, only major health care institutions such as Geisinger, the Mayo Clinic, and Kaiser Permanente incorporate analytics into day-to-day decisions. Research facilities employ analytics teams for clinical research, but perhaps not so much for day-to-day operations. Large health care providers can afford departments of analysts, but most facilities — including those forming accountable care organizations — cannot.
Imagine that you are running a large hospital and are awake nights worrying about the Medicare penalty for readmitting patients within 30 days of their discharge. Now imagine you have access to analytics that can identify about 40 measures that combine to predict a readmission, and a convenient interface is available to tell clinicians in a simple way which patients are most at risk of readmission. Better still, the interface suggests specific interventions to reduce readmissions risk: giving the patient a 30-day supply of medication, arranging transportation to rehab appointments, etc. Read more…
A new report describes an imminent shift in real-time applications and the data architecture they require.
The era is here: we’re starting to see computers making decisions that people used to make, through a combination of historical and real-time data. These streams of data come together in applications that answer questions like:
- What news items or ads is this website visitor likely to be interested in?
- Is current network traffic part of a Distributed Denial of Service attack?
- Should our banking site offer a visitor a special deal on a mortgage, based on her credit history?
- What promotion will entice this gamer to stay on our site longer?
- Is a particular part of the assembly line overheating and need to be shut down?
Such decisions require the real-time collection of data from the particular user or device, along with others in the environment, and often need to be done on a per-person or per-event basis. For instance, leaderboarding (determining who is top candidate among a group of users, based on some criteria) requires a database that tracks all the relevant users. Such a database nowadays often resides in memory. Read more…
Still reflects conventions going back to original adoption of Objective-C
Like many Apple programmers (and new programmers who are curious about iOS), I treated Apple’s Swift language as a breath of fresh air. I welcomed when Vandad Nahavandipoor updated his persistently popular iOS Programming Cookbook to cover Swift exclusively. But I soon realized that the LLVM compiler and iOS runtime have persistent attributes of their own that have not gone away when programmers adopt Swift. This post tries to alert new iOS programmers to the idiosyncrasies of the runtime that they still need to learn.
High-performing memory throws many traditional decisions overboard
Over the past decade, SSD drives (popularly known as Flash) have radically changed computing at both the consumer level — where USB sticks have effectively replaced CDs for transporting files — and the server level, where it offers a price/performance ratio radically different from both RAM and disk drives. But databases have just started to catch up during the past few years. Most still depend on internal data structures and storage management fine-tuned for spinning disks.
Citing price and performance, one author advised a wide range of database vendors to move to Flash. Certainly, a database administrator can speed up old databases just by swapping out disk drives and inserting Flash, but doing so captures just a sliver of the potential performance improvement promised by Flash. For this article, I asked several database experts — including representatives of Aerospike, Cassandra, FoundationDB, RethinkDB, and Tokutek — how Flash changes the design of storage engines for databases. The various ways these companies have responded to its promise in their database designs are instructive to readers designing applications and looking for the best storage solutions.
Competition, access to bandwidth, and other issues muddy the net neutrality waters.
It was the million comments filed at the FCC that dragged me out of the silence I’ve maintained for several years on the slippery controversy known as “network neutrality.” The issue even came up during President Obama’s address to the recent U.S.-Africa Business forum.
Most people who latch on to the term “network neutrality” (which was never favored by the experts I’ve worked with over the years to promote competitive Internet service) don’t know the history that brought the Internet to its current state. Without this background, proposed policy changes will be ineffective. So, I’ll try to fill in some pieces that help explain the complex cans of worms opened by the idea of network neutrality.
Buildings are ready to be smart — we just need to collect and monitor the data.
Buildings, like people, can benefit from lessons built up over time. Just as Amazon.com recommends books based on purchasing patterns or doctors recommend behavior change based on what they’ve learned by tracking thousands of people, a service such as Clockworks from KGS Buildings can figure out that a boiler is about to fail based on patterns built up through decades of data.
I had the chance to be enlightened about intelligent buildings through a conversation with Nicholas Gayeski, cofounder of KGS Buildings, and Mark Pacelle, an engineer with experience in building controls who has written for O’Reilly about the Internet of Things. Read more…
Business models and sustainability will drive success in the health games space.
These efforts have born fruit, and clinical trials have shown the value of many such games. Ben Sawyer, who founded the Games for Health conference more than 10 years ago, is watching all the pieces fall into place for the widespread adoption of games. Business plans, platforms, and the general environment for the acceptance of games (and other health-related apps) are coming together.