How Hadooped is Teradata? Part 4

Consider the Teradata SQL-H implementation using these criteria.
First, Teradata has effective parallel pipes to move data from HDFS to the Teradata database with one pipe per node. There does not seem to be any inter-node IO parallelism. This is a solid feature.

There is a limited ability to push down predicates… SQL-H does allow data to be partitioned on the HDFS side and it will perform partition elimination if the query explicitly calls out a predicate within a partionfilter() keyword. In addition there is an ability to project out columns using a columns() keyword to explicitly specify the columns to be returned. These features are klunky but effective. You would expect partitions to be eliminated when the partitioning column is referenced with a predicate in the query like any other query… and you would expect columns to be projected out if they are not referenced. Normal SQL predicates are applied after the data is moved over the network but before every record is written into the Teradata database.

Finally SQL-H provides no advanced capabilities to push down join operators or other functions.
The bottom line: SQL-H is a sort of klunky implementation, requiring non-ANSI-standard and non-Teradata standard SQL syntax. Predicate push down is limited but better than nothing. As you will see when we review other products, SQL-H is a basic offering. The lack of full predicate push-down and advanced features will negatively and severely impact performance when accessing large volumes of data, Big Data, and the special SQL syntax will limit the ability to access HDFS data from 3rd party tools. This performance penalty will force customers to pre-join and pre-aggregate data in Hadoop rather than access it naturally.

Comments

How to construct a File System that lives in Shared Memory.

Shared Memory File System Goals 1. MOUNTED IN SHARED MEMORY The result is a very fast, real time file system. We use Shared Memory so that the file system is public and not private. 2. PERSISTS TO DISK When the file system is unmounted, what happens to it? We need to be able to save the file system so that a system reboot does not destroy it. A great way to achieve this is to save the file system to disk. 3. EXTENSIBLE IN PLACE We want to be able to grow the file system in place. 4. SUPPORTS CONCURRENCY We want multiple users to be able to access the file system at the same time. In fact, we want multiple users to be able to access the same file at the same time. With the goals now in mind we can now talk about the major design issues: FAT File System & Design Issues The FAT File System has been around for quite some time. Basically it provides a pretty good file structure. But I have two problems with it: 1. FAT IS NOT EXTENSIBLE IN PLAC...

Common Sense Identification of the Security Problems

Organizations make key information security mistakes, which leads to inefficient and ineffective control environment. High profile data breaches and cyber-attacks drive the industry to look for more comprehensive protection measures since many organizations feel that their capability to withstand persistent targeted attacks is minimal. But at the same time, these organizations make some key information security mistakes, that jeopardize their efforts towards control robustness. Although many firms invest in security technologies and people, no one has the confidence that the measures taken are good enough to protect their data from compromises. Below are the 10 worst mistakes which are common to find, and important to address in the path of mature information security posture. If you analyze the cyber security scenarios, and organizational capabilities, the prevailing trend is a vendor-driven approach. In many cases, security professionals adopt the attitude of procuring...

Ingesting IoT Sensor Data Into S3 With an RPI3

StreamSets Data Collector Edge is a lightweight agent used to create end-to-end data flow pipelines. We'll use it help stream data collected from a sensor. Due to the increasing amount of data produced from outside source systems, enterprises are facing difficulties in reading, collecting, and ingesting data into a desired, central database system. An edge pipeline runs on an edge device with limited resources, receives data from another pipeline or reads the data from the device, and controls the device based on the data. StreamSets Data Collector (SDC) Edge, an ultra-lightweight agent, is used to create end-to-end data flow pipelines in StreamSets Data Collector and to run the pipelines to read and export data in and out of systems. In this blog, StreamSets Data Collector Edge is used to read data from an air pressure sensor (BMP180) from an IoT device (Raspberry Pi3). Meanwhile, StreamSets Data Collector is used to load the data into Amazon's Simple Storage Service ...

Big Data and Cloud Computing

Search This Blog