14 Sep

Pivotal Greenplum: Life in a Vacuum by Howard Goldberg

Vacuuming your home is a laborious task that you would rather not do.  However, vacuuming your home is an essential chore that must be done. The same is true for vacuuming the catalog in a Pivotal Greenplum database (“Greenplum”). The proper maintenance and care is required for the Greenplum catalog to keep the database functioning at its peak efficiency. Read More

13 Sep

Introduction of Readable External Protocol of gpfdist

As the fundamental of all ETL operation of Greenplum, it worth explaining a little more  about the detail of gpfdist to understand why it is faster than other tools and how could we improve in future.

This blog will focus on the detail of communication of readable external table between gpfdist server and Greenplum, and introduce the traffic flow and protocol of gpfdist external table. Read More

06 Sep

Introduction to Greenplum ETL tool – Overview

Why ETL is important for Greenplum

As a data warehouse product of future, Greenplum is able to process huge set of data which is usually in petabyte level, but Greenplum can’t generate such number of data by itself. Data is often generated by millions of users or embedded devices. Ideally, all data sources populate data to Greenplum directly  but it is impossible in reality because data is the core asset of a company and Greenplum is only one of many tools that can be used to create value with data asset. One common solution is to use an intermediate system to store all the data.  Read More

05 Sep

On-Demand Machine Learning

Achieving Machine Learning Nirvana
By Shailesh Doshi

Recently, I have been in multiple discussions with clients who want to achieve consistent operationalized data science and machine learning pipelines while the business demands more ‘on-demand’ capability.

Often the ‘on-demand’ conversation starts with ‘Apache Spark’ type usage for analytics use cases but then eventually lead to a desire for an enterprise framework with following characteristics:

  • On-demand resource allocation (spin up/recycle)
  • Data as a service (micro service)
  • Cloud native approach/platform
  • Open Source technology/Open Integration approach
  • Ease of development
  • Agile Deployment
  • Efficient data engineering (minimal movement)
  • Multi–tenancy (resource sharing)
  • Containerization (isolation & security)

Given the complex enterprise landscape, the solution is to look at People, Process and Technology, combined to achieve Machine Learning ‘nirvana’. Read More

21 Aug

Data-Driven Automation in Spring

Data-Driven Software Automation
By Kyle Dunn

Most of us don’t give much thought to elevator rides and the data-driven nature of them. A set of sensors informs precise motor control for acceleration and deceleration, providing a comfortable ride and an accurate stop at your desired floor. Too much acceleration brings the roller coaster experience to near the office but too little will make you late for your team meeting; a good balance of these two can be quite complex in practice. Read More

20 Aug

Short-circuiting the Java stack trace search

PCF Application Log Analytics
By Kyle Dunn

Many developers agree Java stack traces are the source of headaches and needless screen scrolling. Occasionally the verbosity is warranted and essential for debugging, although, more often, the overwhelming detail is just that, overwhelming. In the spirit of better developer productivity and shorter debugging cycles, this post will demonstrate an increasingly relevant reference architecture for cognitive capabilities in Pivotal Cloud Foundry (PCF) using two of Pivotal’s flagship data products: GemFire, an in-memory data grid, and Greenplum, a scale-out data warehouse. Read More

20 Aug

Some Bits on PXF Plugins

“Occasionally it becomes desirable and necessary…to make real what currently is merely imaginary”
By Kyle Dunn

If you’ve not heard already, Pivotal eXtensible Framework, or PXF (for those of you with leftover letters in your alphabet soup), is a unified (and parallel) means of accessing a variety of data formats stored in HDFS, via a REST interface. The code base is a part of Apache HAWQ, where it was originally conceived to bridge the gap between HAWQ’s lineage (Greenplum DB on Hadoop) and the ever-growing menu of storage formats in the larger Hadoop ecosystem. Both Greenplum DB and HAWQ use binary storage formats derived from PostgreSQL 8.2 (as of this writing), whereas Hadoop supports a slew of popular formats: plain text delimited, binary, and JSON document, just to name a few too many. To restate more concisely, PXF is an API abstraction layer on top of disparate HDFS data storage formats. Read More

19 Aug

Going Beyond Structured Data with Pivotal Greenplum

Processing Semi-Structured & Unstructured Data with Mature MPP
By Pravin Rao

When you think about data in a relational data management system, you think of a structured data model organized in rows and columns that fit neatly into a table. While relational databases excel at managing structured data, their rigidity often causes headaches for organizations with diverse forms of data. Businesses often engineer complex data integration processes leveraging ETL tools, Hadoop components, or custom scripts to transform semi-structured data before ingest into a structured database. Read More