Skip to main content

Using Teradata’s Appliance for Hadoop to Reduce TCO

Teradata has recently announced a very complete Teradata database-to-Hadoop integration. Is this note we’ll consider how a Teradata shop might effectively use these features to significantly reduce the TCO of any Teradata system.
 
The Teradata Appliance for Hadoop (here) offering is quite well thought out and complete… including a Teradata appliance, a Hadoop appliance, and the new QueryGrid capability to seamlessly connect the two… so hardware, software, support, and services are all available in very easy-to-consume bundles.
 
There is little published on the details of the QueryGrid feature… so I cannot evaluate where it stands on the query integration maturity curve (see here)… but it certainly provides a significant advance over the current offering (see here and Dan Graham’s associated comments).
 
I believe that there is some instant financial gratification to be had by existing Teradata customers from this Hadoop mashup. Let’s consider this…
Before the possibility of a Hadoop annex to Teradata, Teradata customers had no choice but to store cold, old, data in the Teradata database. If, on occasion, you wanted to perform year by year comparisons over ten years of data then you needed to keep ten years of data in the database at a rough cost of $50K/TB (see here) … even if these queries were rarely executed and were not expected to run against a high performance service level requirement. If you wanted to perform some sophisticated predictive analysis against this data it had to be online. If fact, the Teradata mantra… one which I wholeheartedly agree with… suggests that you really should keep the details online forever as the business will almost always find a way to glean value from this history.
 
This mantra is the basis of what the Hadoop vendors call a data lake. A data warehouse expert would quickly recognize a data lake as a staging area for un-scrubbed detailed data… with the added benefit that a Hadoop-based data lake can store and process data at a $1K/TB price point… and this makes it cost-effective to persist the staged data online forever.
So what does this mean to a Teradata EDW owner? Teradata has published numbers (here) suggesting that 92% of the queries in an EDW only touch 20% of the data. I would suggest that there is some sort of similar ratio that holds for 90% of the remaining queries… they may touch only another 40% of the data. This suggests that the 40% of the data remaining is online to service less than 1% of the queries… and I suggest that these queries can be effectively serviced from the $1K/TB Hadoop annex.
In other words, almost every Teradata shop can immediately benefit from Teradata’s new product announcements by moving 40% of their Teradata database data to Hadoop. Such a move would free Teradata disk space and likely take pressure off to upgrade the cluster. Further, when an upgrade is required, users can reduce the disk footprint of the Teradata database side of the system; add a Hadoop annex, and significantly reduce the TCO of the overall configuration.
Some time back I suggested that Teradata would be squeezed by Hadoop (here and here). To their credit Teradata is going to try and mitigate the squeeze. But the economics remain… and Teradata customers should seriously consider how to leverage the low $/TB of Teradata’s Hadoop offering to reduce costs. Data needs to reside in the lowest cost infrastructure that still provides the required level of service… and the Teradata Hadoop integration provides an opportunity to leverage a new, low-cost, infrastructure.

Comments

Post a Comment

Popular posts from this blog

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
Post a Comment
Read more

Kubernetes Configuration Provider to load data from Secrets and Config Maps

Using Kubernetes Configuration Provider to load data from Secrets and Config Maps When running Apache Kafka on Kubernetes, you will sooner or later probably need to use Config Maps or Secrets. Either to store something in them, or load them into your Kafka configuration. That is true regardless of whether you use Strimzi to manage your Apache Kafka cluster or something else. Kubernetes has its own way of using Secrets and Config Maps from Pods. But they might not be always sufficient. That is why in Strimzi, we created Kubernetes Configuration Provider for Apache Kafka which we will introduce in this blog post. Usually, when you need to use data from a Config Map or Secret in your Pod, you will either mount it as volume or map it to an environment variable. Both methods are configured in the spec section or the Pod resource or in the spec.template.spec section when using higher level resources such as Deployments or StatefulSets. When mounted as a volume, the contents of the Secr...
Post a Comment
Read more

Andriod Bug

A bug that steals cash by racking up charges from sending premium rate text messages has been found in Google Play.  Security researchers have identified 32 apps on Google Play that harbour the bug called BadNews. A security firm Lookout, which uncovered BadNews, said that the malicious program lays dormant on handsets for weeks to escape detection.  The malware targeted Android owners in Russia, Ukraine, Belarus and other countries in eastern Europe. 32 apps were available through four separate developer accounts on Google Play. Google has now suspended those accounts and it has pulled all the affected apps from Google Play, it added. Half of the 32 apps seeded with BadNews are Russian and the version of AlphaSMS it installed is tuned to use premium rate numbers in Russia, Ukraine, Belarus, Armenia and Kazakhstan.
Post a Comment
Read more