Group items tagged

"Google Container Registry provides secure, private Docker image storage on Google Cloud Platform. While Docker provides a central registry to store public images, you may not want your images to be accessible to the world. In this case, you must use a private registry. The Google Container Registry runs on Google Cloud Platform, so can be relied upon for consistent uptime and security. The registry can be accessed through an HTTPS endpoint, so you can pull images from any machine, whether it's a Google Compute Engine instance or your own hardware."

"Terraform is a tool for building, changing, and versioning infrastructure safely and efficiently. Terraform can manage existing and popular service providers as well as custom in-house solutions. Configuration files describe to Terraform the components needed to run a single application or your entire datacenter. Terraform generates an execution plan describing what it will do to reach the desired state, and then executes it to build the described infrastructure. As the configuration changes, Terraform is able to determine what changed and create incremental execution plans which can be applied. The infrastructure Terraform can manage includes low-level components such as compute instances, storage, and networking, as well as high-level components such as DNS entries, SaaS features, etc. The key features of Terraform are: Infrastructure as Code: Infrastructure is described using a high-level configuration syntax. This allows a blueprint of your datacenter to be versioned and treated as you would any other code. Additionally, infrastructure can be shared and re-used. Execution Plans: Terraform has a "planning" step where it generates an execution plan. The execution plan shows what Terraform will do when you call apply. This lets you avoid any surprises when Terraform manipulates infrastructure. Resource Graph: Terraform builds a graph of all your resources, and parallelizes the creation and modification of any non-dependent resources. Because of this, Terraform builds infrastructure as efficiently as possible, and operators get insight into dependencies in their infrastructure. Change Automation: Complex changesets can be applied to your infrastructure with minimal human interaction. With the previously mentioned execution plan and resource graph, you know exactly what Terraform will change and in what order, avoiding many possible human errors."

"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"

"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."

Relevamiento y comparativa de las distribuciones de Hadoop disponibles hasta Agosto 2012.

"A Scala productivity framework for Hadoop."

"Faunus is a property graph analytics engine based on Hadoop. A breadth-first version of the graph traversal language Gremlin operates on a vertex-centric property graph data structure. Faunus can be extended with new operations written using MapReduce and Blueprints."

"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."

"ScalaLab: Matlab-like scientific computing in Scala"

SoftNAS™ is business-class network attached storage (NAS) delivered as software as a service.  The NAS software virtual appliance runs within your cloud computing environment (e.g., Amazon EC2) or local virtual server environment (e.g., VMware vSphere) and manages the available raw disk storage, providing enterprise-grade storage capabilities. SoftNAS leverages a number of open source technologies, including ZFS on Linux.  ZFS on Linux is a port of the popular ZFS filesystem originally produced by Sun Microsystems and released with OpenSolaris.  SoftNAS builds on the robust, solid foundation of ZFS and its powerful, extensible Linux foundation.

"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

"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."

"Datomic is a distributed database designed to enable scalable, flexible and intelligent applications, running on next-generation cloud architectures. It does this by: Bringing declarative data manipulation into the application, and the data with it Getting time, process and perception right Process (writes) require coordination Perception (reads) require none The past doesn't change Leveraging immutability, and a sound model of state Datomic has: ACID Transactions Joins A sound data model A logical query language - Datalog Thus, Datomic avoids the compromises and losses of many NoSQL solutions. In addition, it offers flexibility and power over the traditional model in supporting: Hierarchy Multi-valued attributes Minimal schema Reliable operation on unreliable, ephemeral cloud instances Time Datomic avoids manual caching and replication, complex configuration, sharding (automatic or manual), logging, locking, latching and disk management of traditional servers."

"LiMux - The IT evolution is a project by the city of Munich (third-largest city in Germany) to migrate their software systems from closed-source, proprietary Microsoft products to free and open-source software. The project was successfully completed in late 2013, which involved migrating 15,000 personal computers and laptops of public employees to free and open-source software."