Alteryx ETL

Alteryx for Visual Analytics delivers a single platform that business analysts can use to blend and analyze data from any source and automatically deliver the best possible dataset to Tableau.

new BI

DataHero, GoodData, Chartio,  and ZoomData are examples of the new BI companies offering custom live dashboards.

Qlikview with Google BigQuery

See this post in the Big Data/BI Zone on using Qlikview with Google BigQuery.

Kiji HBase app framework

Kiji is a framework for building big data applications that provides a Java API, command-line tools and DSLs (domain-specific languages) for developers. Built on top of HBase, Kiji scales linearly to support the largest workloads, but also provides simple interfaces and easy to manage environment to build the best big data applications.

BigQuery JDBC Driver

Starschema's high performance Google BigQuery JDBC Driver provides methods for querying and updating data in a Google BigQuery database.

GraphLab distributed graph database

GraphLab is a graph-based, high performance, distributed computation framework written in C++.  While GraphLab was originally developed for Machine Learning tasks, it has found great success at a broad range of other data-mining tasks; out-performing other abstractions by orders of magnitude.
GraphLab Features:

• A unified multicore and distributed API: write once run efficiently in both shared and distributed memory systems
• Tuned for performance: optimized C++ execution engine leverages extensive multi-threading and asynchronous IO
• Scalable: GraphLab intelligently places data and computation using sophisticated new algorithms
• HDFS Integration: Access your data directly from HDFS
• Powerful Machine Learning Toolkits: Turn BigData into actionable knowledge with ease

Shark (Hive on Spark)

Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can answer Hive QL queries up to 100 times faster than Hive without modification to the existing data nor queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions.

Spark

Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write.
To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much more quickly than with disk-based systems like Hadoop MapReduce.
To make programming faster, Spark provides clean, concise APIs in Scala, Java and Python. You can also use Spark interactively from the Scala and Python shells to rapidly query big datasets.

Tachyon distributed file system

Tachyon is a fault tolerant distributed file system enabling reliable file sharing at memory-speed across cluster frameworks, such as Spark and MapReduce. It achieves its high performance by leveraging lineage information and using memory aggressively. Tachyon caches working set files in memory, and enables different jobs/queries and frameworks to access cached files at memory speed. Thus, Tachyon avoids going to disk to load datasets that are frequently read.