Group items tagged

"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."

"Bandicoot is an open source programming system with a new set-based programming language, persistency capabilities, and run-time environment. The language is similar to general purpose programming languages where you write functions/methods and access data through variables. Though, in Bandicoot, you always manipulate data in sets using a small set-based algebra (the relational algebra)." "Here are the main features:   - functions are automatically exposed via HTTP using CSV for data, e.g. /List, /Append  - supports persistency via global variables (with transactions and ACID)  - can run on multiple computers to scale up the read throughput  - built in operators from the relational algebra with a simple syntax, e.g. "+" (union), "-" (minus)  - small binary ~100KB"

"I published two such Tables of Elements about a year ago, click here to check them out. This one is a new one, focusing on machine learning libraries (R and Julia). And it is interactive, with access to the various libraries listed in the table, when clicking on an element (only on the original article)."

"FreeIPA is an integrated security information management solution combining Linux (Fedora), 389 Directory Server, MIT Kerberos, NTP, DNS, Dogtag (Certificate System). It consists of a web interface and command-line administration tools. FreeIPA is an integrated Identity and Authentication solution for Linux/UNIX networked environments. A FreeIPA server provides centralized authentication, authorization and account information by storing data about user, groups, hosts and other objects necessary to manage the security aspects of a network of computers. FreeIPA is built on top of well known Open Source components and standard protocols with a very strong focus on ease of management and automation of installation and configuration tasks. Multiple FreeIPA servers can easily be configured in a FreeIPA Domain in order to provide redundancy and scalability. The 389 Directory Server is the main data store and provides a full multi-master LDAPv3 directory infrastructure. Single-Sign-on authentication is provided via the MIT Kerberos KDC. Authentication capabilities are augmented by an integrated Certificate Authority based on the Dogtag project. Optionally Domain Names can be managed using the integrated ISC Bind server. Security aspects related to access control, delegation of administration tasks and other network administration tasks can be fully centralized and managed via the Web UI or the ipa Command Line tool."

"The motivation for adding security to Apache Hadoop actually had little to do with traditional notions of security in defending against hackers since all large Hadoop clusters are behind corporate firewalls that only allow employees access. Instead, the motivation was simply that security would allow us to use Hadoop more effectively to pool resources between disjointed groups. Larger clusters are much cheaper to operate and require fewer copies of duplicated data."

"The Cloudera Connector for Qlikview enables your Enterprise's power users to access Hadoop data through the Qlikview 11.2. The driver achieves this by translating Open Database Connectivity (ODBC) calls from Qlikview into HiveQL queries. The driver supports CDH 4.1."

"Prisma replaces traditional ORMs"

Documentación de referencia de SLICK 1.0.0 (ex scalaquery) recientemente liberado. Pegarle una mirada.

Un tutorial profundo de SLICK (ex-ScalaQuery, la librería de acceso a RDBMS type-safe que usamos en varios proyectos basados en scala incluyendo Navrel, etc.). Los que no conozcan SLICK/ScalaQuery deberían verlo aprovechando este nuevo material.

"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

"Reactive type-safe Scala driver for Neo4J"