- MillWheel: Fault-Tolerant Stream Processing at Internet Scale — Google Research paper on the tech underlying the new cloud DataFlow tool. Watch the video. Yow.
- The Integer Overflow Bug That Went to Mars — long-standing (20 year old!) bug in a compression library prompts a wave of new releases. No word yet on whether NASA will upgrade the rover to avoid being pwned by Martian script kiddies. (update: I fell for a self-promoter. The Martians will need to find another attack vector. Huzzah!)
- epoch (github) — Fastly-produced open source general purpose real-time charting library for building beautiful, smooth, and high performance visualizations.
- Achieving Rapid Response Times in Large Online Services (YouTube) — Jeff Dean‘s keynote at Velocity. He wrote … a lot of things for this. And now he’s into deep learning ….
ENTRIES TAGGED "Big Data"
Many more companies want to highlight how they're using Apache Spark in production.
One of the trends we’re following closely at Strata is the emergence of vertical applications. As components for creating large-scale data infrastructures enter their early stages of maturation, companies are focusing on solving data problems in specific industries rather than building tools from scratch. Virtually all of these components are open source and have contributors across many companies. Organizations are also sharing best practices for building big data applications, through blog posts, white papers, and presentations at conferences like Strata.
These trends are particularly apparent in a set of technologies that originated from UC Berkeley’s AMPLab: the number of companies that are using (or plan to use) Spark in production1 has exploded over the last year. The surge in popularity of the Apache Spark ecosystem stems from the maturation of its individual open source components and the growing community of users. The tight integration of high-performance tools that address different problems and workloads, coupled with a simple programming interface (in Python, Java, Scala), make Spark one of the most popular projects in big data. The charts below show the amount of active development in Spark:
For the second year in a row, I’ve had the privilege of serving on the program committee for the Spark Summit. I’d like to highlight a few areas where Apache Spark is making inroads. I’ll focus on proposals2 from companies building applications on top of Spark.
Agile methodology brings flexibility to the EDW and offers ways to integrate open-source technologies with existing systems.
Data analysis, like other pursuits, is a balancing act. The rise of big data ratchets up the pressure on the traditional enterprise data warehouse (EDW) and associated software tools to handle rapidly evolving sets of new demands posed by the business. Companies want their EDW systems to be more flexible and more user friendly — without sacrificing processing speeds, data integrity, or overall reliability.
“The more data you give the business, the more questions they will ask,” says José Carlos Eiras, who has served as CIO at Kraft Foods, Philip Morris, General Motors, and DHL. “When you have big data, you have a lot of different questions, and suddenly you need an enterprise data warehouse that is very flexible.”
EDWs are remarkably powerful, but it takes considerable expertise and creativity to modify them on the fly. Adding new capabilities to the EDW generally requires significant investments of time and money. You can develop your own tools internally or purchase them from a vendor, but either way, it’s a hard slog. Read more…