This month's topic: Apache Drill

Apache Drill is a new Apache incubator project. Its goal is to provide a distributed system for interactive analysis of large-scale datasets. Inspired by Google's Dremel technology, it aims to process trillions of records in seconds. We will cover the goals of Apache Drill, its use cases and how it relates to Hadoop, MongoDB and other large-scale distributed systems. We'll also talk about details of the architecture, points of extensibility, data flow and our first query languages (DrQL and SQL).

Speaker: Gera Shegalov Gera Shegalov owns Hadoop MapReduce and Hadoop Core components in MapR's Hadoop Distribution. Prior to MapR, he worked at Oracle in Oracle Database High Availability on (Active) Data Guard, and in Oracle Java Platform Group on JMS backend communication and storage. Gera received Masters and PhD in Computer Science from Saarland University in Saarbruecken, Germany. His research focussed on workflow management, temporal databases, as well as application & database recovery.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is A time-efficient, linear-space local similarity algorithm by Huang and Miller. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets by Yankov, Keogh and Rebbapragada. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent by Gemulla, Haas, Nijkamp and Sismanis. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Computational Methods for Dynamic Graphs by Cortes, Pregibon, and Volinsky. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Ad Click Prediction: a View from the Trenches by McMahan et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget by Korattikara, Chen, and Welling. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

CANCELED! Forecast is calling for 3-7 inches of snow tonight, the sinus plague is going around, and it's just damn cold. See you in March!

"No talks. One paper per month, no obligation to read it."

This month's paper is Image Mining of Historical Manuscripts to Establish Provenance by Hu et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Image Mining of Historical Manuscripts to Establish Provenance by Hu et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Palette Power: Enabling Visual Search through Colors by Bhardwaj et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Profiler: Integrated Statistical Analysis and Visualization for Data Quality Assessment by Kandel et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions by Wilkinson et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. One paper per month, no obligation to read it."

This month's paper is Mondrian Forests: Efficient Online Random Forests by Lakshminarayanan et al. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Dynamo: Amazon’s Highly Available Key-value Store by DeCandia etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is A Crowd of Your Own: Crowdsourcing for On-Demand Personalization by Organisciak etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Beyond Clicks: Dwell Time for Personalization by Yi etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Materialization Strategies in a Column-Oriented DBMS by Abadi etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Large-Scale High-Precision Topic Modeling on Twitter by Yang etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction by Shen etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be pizza.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization by Compton etal. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be food.

"No talks. You may opt to take up to 60 seconds to complain about Big Data. One paper per month, no obligation to read it."

We'll start by letting anyone who wants to take up to a minute to tell us what they've been doing with data lately.

This month's paper is Feature Selection For High-Dimensional Clustering by Wasserman, Azizyan, and Singh. Read it or don't - the goal is just to have something to start conversations. "Did you read the paper?" will do nicely.

Mention @PDXBigData on Twitter with the link to the full paper to suggest papers for future sessions.

There will be food.

Michael Wall Polybase – It’s What’s for Dinner

Polybase is Microsoft’s great new replacement to linked servers. With the current version in SQL Server 2019, one can connect to multiple data sources such as Teradata, Apache, Hadoop, Oracle, and many many more. We will review best practices for setting up Polybase and its Scale-Out groups as well as showing how it interacts in an AlwaysOn Availabilty Group. When Scaling-Out (up!), Polybase is able to work harder and deliver more data. What happens when one of its node disappears? We will have a live demo demonstrating how to manage the Scale-Out group when bad things happen. In addition, the DMVs for managing Polybase will be reviewed, and finally, there will be results of the impact of using multiple nodes in the Scale-Out group on the Polybase queries!