From big data to fast data
Designing application architectures for real-time decisions.
Big Data Tools and Pipelines
Ideas and resources related to data tools.
How to search in the MarkLogic database
There are advantages to having a search engine built into a database.
Geospatial data and analysis for disaster relief
Tools from maps to drones respond to crises with increasing speed and accuracy.
Understanding projections with spatial and geo data
Transform your basemaps using CARTO and PostGIS.
Extend Spark ML for your own model/transformer types
How to use the wordcount example as a starting point (and you thought you’d escape the wordcount example).
A new visualization to beautifully explore correlations
Introducing the solar correlation map, and how to easily create your own.
A guide to Elasticsearch 5.0 and the ELK/Elastic Stack
Learn how to ship, parse, store, and analyze logs.
Enabling enterprise-wide data analytics
Performing business analytics on the data lake using next-gen open source tools.
Data science gophers
Python and R are widely accepted as logical languages for data science—but what about Go?
Capturing semantic meanings using deep learning
Word embedding in natural language processing.
Analysis without boundaries
Apache Arrow makes it possible to use multiple languages and heterogeneous data infrastructure.
Why you need another database
An analytics database can offer performance and scalability advantages.
Extend structured streaming for Spark ML
Early methods to integrate machine learning using Naive Bayes and custom sinks.
5 mistakes to avoid when deploying an analytical database
Leading data-driven organizations point out five common pitfalls.
Spark comparison: AWS vs. GCP
Assessing cost, performance, and run time of a typical Spark workload.
O’Reilly Online Training: SQL Fundamentals for Data
October 4-5, 2016, join Thomas Nield for a hands-on course for beginners on core database and SQL fundamentals.
Hadoop: What you need to know
Learn about the basics of how Hadoop works, why it's such an important technology, and how you should be using it without getting mired in the details.
Best practices for streaming applications
Mark Grover and Ted Malaska offer an overview of projects for streaming applications, including Kafka, Flume, and Spark Streaming, and discuss the architectural schemas available, such as Lambda and Kappa.
Scalable data science with R
You’ve got three options: Scaling up, scaling out, or using R as an abstraction layer.
Running Spark on Alluxio with S3
Calvin Jia presents an in-depth overview of Alluxio and its role in the big data ecosystem. In this segment, he reviews examples that show how Alluxio complements Spark and S3, to enable fast data access.
Uber’s case for incremental processing on Hadoop
Near-real-time processing yields increased efficiency and an opportunity for unified architecture.
Accelerating Spark workloads using GPUs
Transparent matching of Spark portability with GPU performance.
The evolution of advanced analytics in Spark
Addressing the challenge of delivering big data analytics to the masses.
Twitter’s real-time data stack
In this excerpt, Karthik Ramasamy and Sijie Guo of Twitter discuss the operational experience of DistributedLog and Heron, two powerful real-time analytics tools that were open sourced by the company in early 2016.