Frequently Asked Questions, Articles, and Downloads
Advanti offers a collection of resources about Big Data analytical infrastructure to help analysts in finance.
- Discover quick answers to commonly asked questions about Big Data analytical infrastructure by visiting the FAQ section.
- Stay up to date on the lastest trends in Big Data analytical infrastructure and solutions to common problems plaguing quantitative financial analysts by reading Blog articles written by the Advanti team and guest authors.
- Find downloadable resources that will help you improve your systematic investment process by visiting the A-Cubed section.
Blog Post Timeline
- DIY Instructions for Installing Hadoop, Spark, and Hive on Ubuntu
- Doug to speak at Open Data Science Conference in Boston about Spark, Python, and Parquet
- How to kickstart a data-driven development project
- Advanti’s Stanislav Seltser teaches a graduate program on Modern Data Warehousing
- Bloomberg Open Symbology
- Advanti Simplifies and Accelerates the Research Process for Investment Firms
- “More Than Just Data” at Cutter Associates Conference
- ASTERIX – A Highly Scalable Parallel Platform for Semi-structured Data Management and Analysis
- Installing Greenplum Database Community Edition (CE) 184.108.40.206 to Mac OS X 10.8.2
- Dilbert & Big Data
- A Quick Primer of KDB+
- Bi-Temporality / Point-in-Time
- ISIN Security Identifier Symbology
- 2013 New England Database Day
- Vertica vs. SQL Server Feature Comparison
- Because your Research data has value
- Advantages of Column-Stores over Row-Stores for Data Warehousing and Analytical Applications
- What’s Inside of I/B/E/S Earnings Estimates Data from Thomson Reuters?
- Douglas Eisenstein Enters the “Shark Tank” and Wins
- PyData NYC 2012
- Big Data Finance Boston
- Five Pervasive Problems
- Problems with Preparing, Constructing, and Managing Derived Data in Finance
- Data Management Problems in Data-Intensive Finance
- Data Access and Manipulation Problems in Research Languages
- Historical Security Master Problems
- Pervasive Problems in Data-Intensive Finance