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"These are the major new features added since Slick 1.0.1: A code generator that reverse-engineers the database schema and generates all code required for working with Slick. New driver architecture to allow support for non-SQL, non-JDBC databases. Table definitions in the Lifted Embedding use a new syntax which is slightly more verbose but also more robust and logical, avoiding several pitfalls from earlier versions. Table definitions (and their * projections) are not restricted to flat tuples of columns anymore. They can use any type that would be valid as the return type of a Query. The old projection concatenation methods ~ and ~: are still supported but not imported by default. In addition to Scala tuples, Slick supports its own HList abstraction for records of arbitrary size. You can also add support for your own record types with only a few lines of code. All record types can be used everywhere (including table definitions and mapped projections) and they can be mixed and nested arbitrarily. Soft inserts are now the default, i.e. AutoInc columns are automatically skipped when inserting with +=, ++=, insert and insertAll. This means that you no longer need separate projections (without the primary key) for inserts. There are separate methods forceInsert and forceInsertAll in JdbcProfile for the old behavior. A new model for pre-compiled queries replaces the old QueryTemplate abstraction. Any query (both, actual collection-valued Query objects and scalar queries) or function from Column types to such a query can now be lifted into a Compiled wrapper. Lifted functions can be applied (without having to recompile the query), and you can use both monadic composition of Compiled values or just get the underlying query and use that for further composition. Pre-compiled queries can now be used for update and delete operations in addition to querying. threadLocalSession has been renamed to dynamicSession and the corresponding methods have distinct names (e.g. w

"Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. The table metadata is stored in an HBase table and versioned, such that snapshot queries over prior versions will automatically use the correct schema. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows. "

"Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. The table metadata is stored in an HBase table and versioned, such that snapshot queries over prior versions will automatically use the correct schema. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows."

"GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools."

"Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive's query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones."

"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."

"In this paper we propose the TurboXPath path processor, which accepts a language equivalent to a subset of the for-let-where constructs of XQuery over a single document. TurboXPath can be extended to provide full XQuery support or used to augment federated database engines for efficient handling of queries over XML data streams produced by external sources. Internally, TurboXPath uses a tree-shaped path expression with multiple outputs to drive the execution. The result of a query execution is a sequence of tuples of XML fragments matching the output nodes. Based on a streamed execution model, TurboXPath scales up to large documents and has limited memory consumption for increased concurrency"

"Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook."

"Collection+JSON is a JSON-based read/write hypermedia-type designed to support management and querying of simple collections. It is similar to the The Atom Syndication Format (RFC4287) and the The Atom Publishing Protocol (RFC5023) . However, Collection+JSON defines both the format and the semantics in a single media type. It also includes support for Query Templates and expanded write support through the use of a Write Template."

A simple query library for scala and the Google data store.

"restQL is a microservice query language that makes easy to fetch information from multiple services in the most efficient manner."

"HDF (Hierarchical Data Format) technologies are relevant when the data challenges being faced push the limits of what can be addressed by traditional database systems, XML documents, or in-house data formats. Leveraging the powerful HDF products and the expertise of The HDF Group, organizations realize substantial cost savings while solving challenges that seemed intractable using other data management technologies. Many HDF adopters have very large datasets, very fast access requirements, or very complex datasets. Others turn to HDF because it allows them to easily share data across a wide variety of computational platforms using applications written in different programming languages. Some use HDF to take advantage of the many open-source and commercial tools that understand HDF. Similar to XML documents, HDF files are self-describing and allow users to specify complex data relationships and dependencies. In contrast to XML documents, HDF files can contain binary data (in many representations) and allow direct access to parts of the file without first parsing the entire contents. HDF, not surprisingly, allows hierarchical data objects to be expressed in a very natural manner, in contrast to the tables of relational database. Whereas relational databases support tables, HDF supports n-dimensional datasets and each element in the dataset may itself be a complex object. Relational databases offer excellent support for queries based on field matching, but are not well-suited for sequentially processing all records in the database or for subsetting the data based on coordinate-style lookup."

"Cascalog is a fully-featured data processing and querying library for Clojure or Java. The main use cases for Cascalog are processing "Big Data" on top of Hadoop or doing analysis on your local computer. Cascalog is a replacement for tools like Pig, Hive, and Cascading and operates at a significantly higher level of abstraction than those tools."

"Sublime Plugin for running Neo4j Cypher queries."

"Syntax highlighting for Neo4j's Cypher query language in SublimeText."

"Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals."

This article is a basic list of best practices that our Grails projects follow, gathered from mailing lists, Stack Overflow, blogs, podcasts and internal discussions at IntelliGrape.

Subset is a library to ease extracting fields from MongoDB documents, serializing them back and constructing queries.

Streaming path filter node factory for continuous queries and/or transformations over very large or infinitely long XML input.

Data.js is a data representation framework for Javascript. It is being developed in the context of Substance, a web-based document authoring and publishing engine. It took some inspiration from various existing libraries such as the Google Visualization API or Underscore.js.  You can report bugs and discuss features on the GitHub issues page, on Freenode IRC in the #_substance chann el, post questions to the Google Group, or send tweets to @_substance. With Data.js you can: Model your domain data using a simple graph-based object model that can be serialized to JSON. Traverse your graph, including relationships using a simple API. Manipulate and query data on the client (browser) or on the server (Node.js) by using exactly the same API.