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Promising extra thorough and timelier facts evaluation, Hewlett-Packard has launched a software kit that combines the enterprise's Vertica database with its IDOL facts evaluation platform.
The HP Haven Connector Framework Server can moreover allow companies to dissect information units that were too huge or unwieldy to research before.
The tackle provides "a combination of statistical and contextual understanding," of information, said Jeff Veis, HP vice chairman of advertising and marketing for massive data. "that you may drag in any sort of facts, after which attain real-time unreasonable performance analysis."
The Vertica Analytics Platform is columnar database application built for instantly examining tremendous amounts of structured statistics the usage of SQL (Structured query Language). IDOL (clever statistics working Layer) was built to index and search unstructured data within an commercial enterprise.
HP received each Vertica and IDOL in divide offers in 2011.
Integrating Vertica and IDOL opens up a variety of unstructured statistics sources that may moreover subsist scrutinized with Vertica's SQL-primarily based evaluation capabilities, Veis spoke of.
a solid could consume the server application to research divide statistics repositories, including data kept in enterprise programs comparable to Microsoft exchange, Microsoft SharePoint, Oracle and SAP industry purposes, in addition to records within cloud services akin to box.web, Salesforce.com, and Google power.
advertising departments could consume the software to determine trends, or to learn references to keywords of activity, throughout these disparate statistics sources, Veis mentioned.
The connector used to link IDOL with Vertica is one of 50 enhancements that the company has made to its Haven portfolio of data evaluation software and capabilities.
The industry issued HP Haven capabilities Graphing, a device for knowing relationships and connections among numerous entities. The enterprise moreover has a brand novel speech focus utility that makes consume of neural networking to help recognition through as much as 75 percent.
HP declined to pomp the rate of the HP Haven Connector Framework Server.
Bringing some enterprise rigor to the wild world of huge statistics, Hewlett-Packard has issued a kit for you to enable agencies to harness HP’s Vertica analytical database engine to investigate reams of unstructured information live in Hadoop programs.
“We’re marrying the better of both worlds,” pointed out Jeff Healey, director of product advertising and marketing for HP’s large facts platform products. “We’re bringing a proven SQL engine into the Hadoop infrastructure. that you can view and learn Hadoop facts with out getting to know an entire bunch of latest knowledge.”
The HP Vertica for SQL on Hadoop package can toil with the Hortonworks, MapR and Cloudera Hadoop distributions and with simple Apache Hadoop methods that may additionally were developed in condo.
HP had discovered that about half of its Vertica consumer groundwork were additionally drawn to the usage of Hadoop, Healey talked about. In effect, this kit will permit consumers to obtain consume of Vertica as a front proximate for examining information stored in Hadoop deployments.
A column-oriented database gadget, HP Vertica Analytics Platform become designed to quickly execute tremendous-scale analysis jobs. it might probably domain queries written in the commonplace SQL (Structured query Language) recognizable by means of most database administrators and third-party enterprise intelligence tools.
The Vertica engine “is awfully reputable and may supply the stability that commercial enterprise valued clientele are seeking for today,” referred to Ignacio Hwang, HP senior product supervisor.
part of the package is HP’s Flex Zone, which may moreover subsist used to learn unstructured facts with SQL devoid of first going in the course of the toil of applying schemas to the information. shoppers could additionally consume the package to import selected data from Hadoop for quicker evaluation within Vertica itself.
HP will visage quite a lot of competition in offering SQL for Hadoop. other application that addresses this need contains the open-supply Apache Hive, Cloudera’s Impala (additionally open supply), IBM’s BigSQL and Pivotal’s Hawq, among others.
One early consumer of the know-how has been the Snagajob employment carrier, which makes consume of the utility to give a route for its million daily net friends to sort throughout the 400,000 jobs listings that the company automatically maintains posted.
The HP Vertica Analytics Platform is obtainable now. The enterprise did not disclose selected pricing apart from to assert it will subsist priced on a per-node groundwork.To observation on this text and other PCWorld content, quest recommendation from their fb web page or their Twitter feed.
Tandem computers Inc. these days introduced a brand novel data groundwork management gadget providing the capacity to role from diverse terminals in a dispensed mode. Tandem stated the novel system, called nonstop Structured query Language, is each simple to consume and faster than general relational information groundwork systems.
E.J. Codd, who developed the primary relational data groundwork for the international industry Machines service provider within the 1960's and is now an independent consultant, celebrated the introduction changed into a ''dramatic'' undergo that could establish Tandem ''years ahead of the competition.''
In a simulated customer-use check audited by the Codd and Date Consulting community, the Tandem gadget handled more than 200 debit/credit transactions a 2d on 32 tandem processors. A related I.B.M. product accurately yields 40 transactions a 2d, prompting Tandem to pretense a value to performance growth of five instances. complete hardware, application and maintenance prices of the brand novel device can subsist about $eleven million for five years.proceed analyzing the main story
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Today, Structured Query Language is the measure means of manipulating and querying data in relational databases, though with proprietary extensions among the products. The ease and ubiquity of SQL believe even led the creators of many “NoSQL” or non-relational data stores, such as Hadoop, to adopt subsets of SQL or near up with their own SQL-like query languages.
But SQL wasn’t always the “universal” language for relational databases. From the genesis (circa 1980), SQL had certain strikes against it. Many researchers and developers at the time, including me, thought that the overhead of SQL would hold it from ever being practical in a production database.
Clearly, they were wrong. But many still believe that, for All of SQL’s ease and accessibility, the charge exacted in runtime performance is often too high.Before SQL
Before there was SQL, databases had tight, navigational programming interfaces, and typically were designed around a network schema called the Codasyl data model. Codasyl (Committee on Data Systems Languages) was a consortium that was amenable for the Cobol programming language (starting in 1959) and database language extensions (starting 10 years later).
When you programmed against a Codasyl database, you were navigating to records through sets, which express one-to-many relationships. Older hierarchical databases only allow a record to belong to one set. Network databases allow a record to belong to multiple sets.
Say you wanted to list the students enrolled in CS 101. First you would find "CS 101" in the Courses set by name, set that as the owner or parent of the Enrollees set, find the first member (ffm) of the Enrollees set, which is a Student record, and list it. Then you would travel into a loop: Find next member (fnm) and list it. When fnm failed, you would exit the loop.
That may appear affection a lot of scut toil for the database programmer, but it was very efficient at execution time. Experts affection Michael Stonebraker of University of California at Berkeley and Ingres pointed out that doing that sort of query in a Codasyl database such as IDMS took roughly half the CPU time and less than half the memory as the selfsame query on a relational database using SQL.
For comparison, the equivalent SQL query to revert All of the students in CS 101 would subsist something likeSELECT student.name FROM courses, enrollees, students WHERE course.name ="CS 101"
That syntax implies a relational inner associate (actually two of them), as I’ll complicated below, and leaves out some Important details, such as the fields used for the joins.Relational databases and SQL
Why would you give up a doubling of execution accelerate and memory? There were two commodious reasons: ease of development and portability. I didn’t deem either one mattered much in 1980 compared to performance and memory requirements, but as computer hardware improved and became cheaper people stopped caring about execution accelerate and memory and worried more about the cost of development.
In other words, Moore’s Law killed Codasyl databases in favor of relational databases. As it happened, the improvement in development time was significant, but SQL portability turned out to subsist a pipe dream.
Where did the relational model and SQL near from? E.F. “Ted” Codd was a computer scientist at the IBM San Jose Research Laboratory who worked out the theory of the relational model in the 1960s and published it in 1970. IBM was tedious to implement a relational database in an endeavor to protect the revenues of its Codasyl database IMS/DB. When IBM finally started its System R project, the development team (Don Chamberlin and Ray Boyce) wasn’t under Codd, and they ignored Codd’s 1971 Alpha relational language paper to design their own language, Sequel (Structured English Query Language). In 1979, before IBM had even released its product, Larry Ellison incorporated the language in his Oracle Database (using IBM’s pre-launch Sequel publications as his spec). Sequel soon became SQL to avoid an international trademark violation.
The “tom-toms beating for SQL” (as Michael Stonebraker establish it) were coming not only from Oracle and IBM, but moreover from customers. It wasn’t simple to hire or train Codasyl database designers and programmers, so Sequel (and SQL) looked much more attractive. SQL was so attractive in the later 1980s that many database vendors essentially stapled a SQL query processor on top of their Codasyl databases, to the grand quiver of Codd, who felt that relational databases had to subsist designed from scratch to subsist relational.
A simple relational database, as designed by Codd, is built on tuples grouped into relations, consistent with first-order predicate logic. Real-world relational databases believe tables that accommodate fields, constraints, and triggers, and tables are related through exotic keys. SQL is used to declare the data to subsist returned, and a SQL query processor and query optimizer whirl the SQL declaration into a query plot that is executed by the database engine.
SQL includes a sub-language for defining schemas, the data definition language (DDL), along with a sub-language for modifying data, the data manipulation language (DML). Both of these believe roots in early Codasyl specifications. The third sub-language in SQL declares queries, through the SELECT statement and relational joins.The SELECT statement in SQL
The SELECT statement tells the query optimizer what data to return, what tables to study in, what relations to follow, and what order to impose on the returned data. The query optimizer has to pattern out by itself what indexes to consume to avoid brute constrain table scans and achieve generous query performance, unless the particular database supports index hints.
Part of the craft of relational database design hangs on the judicious consume of indexes. If you omit an index for a frequent query, the total database can tedious down under massive read loads. If you believe too many indexes, the total database can tedious down under massive write and update loads.
Another Important craft is choosing a good, unique primary key for every table. You not only believe to reckon the impact of the primary key on common queries, but how it will play in joins when it appears as a exotic key in another table, and how it will move the data’s locality of reference.
In the advanced case of database tables that are split up into different volumes depending on the value of the primary key, called horizontal sharding, you moreover believe to reckon how the primary key will move the sharding. Hint: You want the table distributed evenly across volumes, which suggests that you don’t want to consume date stamps or consecutive integers as primary keys.
Discussions of the SELECT statement may start simple, but can quickly become confusing. Consider:SELECT * FROM Customers;
Simple, right? It asks for All fields and All rows of the Customers table. Suppose, however, that the Customers table has a hundred million rows and a hundred fields, and one of the fields is a large text domain for comments. How long will it acquire to drag down All that data over a 10 megabit per second network connection if each row contains an tolerable of 1 kilobyte of data?
Perhaps you should cleave down how much you dispatch over the wire. Consider:SELECT TOP 100 companyName, lastSaleDate, lastSaleAmount, totalSalesAmount FROM CustomersWHERE situation ="Ohio" AND city ="Cleveland"ORDER BY lastSaleDate DESCENDING;
Now you’re going to drag down a lot less data. You believe asked the database to give you only four fields, to only reckon the companies in Cleveland, and to give you just the 100 companies with the most recent sales. To attain that most efficiently at the database server, however, the Customers table needs an index on state+city for the WHERE clause and an index on lastSaleDate for the ORDER BY and TOP 100 clauses.
By the way, TOP 100 is cogent for SQL Server and SQL Azure, but not MySQL or Oracle. In MySQL, you’d consume limit 100 after the WHERE clause. In Oracle, you’d consume a bound on ROWNUM as partake of the WHERE clause, i.e. WHERE... AND ROWNUM <=100. Unfortunately, the ANSI/ISO SQL standards (and there are nine of them to date, stretching from 1986 to 2016) only travel so far, beyond which each database introduces its own proprietary clauses and features.Relational joins in SQL
So far, I’ve described the SELECT syntax for unique tables. Before I can explain JOIN clauses, you need to understand exotic keys and relations between tables. I’ll complicated this by using examples in DDL, using SQL Server syntax.
The short version of this is fairly simple. Every table that you want to consume in relations should believe a primary key constraint; this can either subsist a unique domain or a combination of fields defined by an expression. For example:CREATE TABLE Persons (PersonID int NOT NULL PRIMARY KEY,PersonName char(80),...
Every table that needs to relate to Persons should believe a domain that corresponds to the Persons primary key, and to preserve relational integrity that domain should believe a exotic key constraint. For example:CREATE TABLE Orders (OrderID int NOT NULL PRIMARY KEY,...PersonID int exotic KEY REFERENCES Persons(PersonID));
There are longer versions of both statements that consume the CONSTRAINT keyword, which lets you appellation the constraint. That’s what most database design tools generate.
Primary keys are always indexed and unique (the domain values cannot subsist duplicated). Other fields can optionally subsist indexed. It’s often useful to create indexes for exotic key fields and for fields that emerge in WHERE and ORDER BY clauses, although not always because of the potential overhead from writes and updates.
How would you write a query that returns All of the orders placed by John Doe?SELECT PersonName, OrderID FROM PersonsINNER associate Orders ON Persons.PersonID = Orders.PersonIDWHERE PersonName ="John Doe";
In fact, there are four kinds of JOIN: INNER, OUTER, LEFT, and RIGHT. The INNER associate is the default (you can omit the word INNER), and it’s the one that includes only rows that accommodate matching values in both tables. If you want to list persons whether or not they believe orders, you’d consume a LEFT JOIN, for example:SELECT PersonName, OrderID FROM PersonsLEFT associate Orders ON Persons.PersonID = Orders.PersonIDORDER BY PersonName;
When you start doing queries that associate more than two tables, consume expressions, or coerce data types, the syntax can win a miniature hairy at first. Fortunately, there are database development tools that can generate amend SQL queries for you, often by dragging and dropping tables and fields from the schema diagram into a query diagram.SQL stored procedures
Sometimes the declarative nature of the SELECT statement doesn’t win you where you want to go. Most databases believe a facility called stored procedures; unfortunately this is an region where the databases pretty much All consume proprietary extensions to the ANSI/ISO SQL standards.
In SQL Server, the initial lingo for stored procedures (or stored procs) was Transact-SQL, aka T-SQL; in Oracle, it was PL-SQL. Both databases believe added additional languages for stored procedures, such as C#, Java, and R. A simple T-SQL stored procedure might only subsist a parameterized version of a SELECT statement. Its advantages are ease of consume and efficiency. Stored procedures are optimized when they are saved, not every time they are executed.
A more complicated T-SQL stored procedure might consume multiple SQL statements, input and output parameters, local variables, BEGIN...END blocks, IF...THEN...ELSE conditions, cursors (row-by-row processing of a set), expressions, temporary tables, and a total host of other procedural syntax. Obviously if the stored procedure language is C#, Java, or R, you’re going to consume the functions and syntax of those procedural languages. In other words, despite the fact that the motivation for SQL was to consume standardized declarative queries, in the existent world you observe lots of database-specific procedural server programming.
That doesn’t quite acquire us back to the heinous veteran days of Codasyl database programming (although cursors near close), but it does walk back from the ideas that SQL statements should subsist standardized and that performance concerns should subsist left to the database query optimizer. In the end, a doubling of performance is often too much to leave on the table.
Structured Query Language (SQL) is a programming language designed for database management and manipulation – primarily used to find and drag information from large databases.
SQL is normally displayed on relational databases, a form of database derived from Edgar F. Codd's relational model of storing information in route that makes it accessible and identifiable in relation to other information in it.© iStock
The language was developed at IBM by Donald Chamberlin and Raymond Boyce in 1974, where it was initially named SEQUEL (Structured English Query Language), pile on Edgar F. Codd’s relational model.
SQL contains four main categories, with an additional two commands that are mostly used for computer databases, and has since been integrated with more features after becoming a measure of the American National Standards Institute (ANSI) in 1986 and International Organisation for Standardisation (ISO) in 1987.
SQL is a fourth-generation language, meaning commands are proximate to human language. By contrast, the Java and C++ programming languages are third-generation languages that are more abstract to the untrained eye.Read next: The best NoSQL database options for the enterprise
SQL has become the industry standard for manipulating and querying data in a relational database. The uses of the language are broken into three other components aside from writing queries. These are:
Data Manipulation Language (DML), which is used to manipulate database content in retrieve, update, delete and insert modes. The DML has four commands that can subsist used to manipulate the information which is the Select, Insert, Update and Delete commands.
Data Definition Language (DDL) is used to manage tables and index structures, moreover with synchronised commands that can subsist used to create, alter and delete database tables.
Data Control Language (DCL) is often used to manage who can access the database, providing rights to certain users. The two commands that near with this are award and Revoke, which are the two core commands of relational database security and authentication.
Data Query Language (DQL) is similar to DML, as it is used to select, insert, update and delete data from the database. The two can subsist used together.
The other SQL commands are data administration and transactional control commands.
The commands, moreover known as statements, can subsist used for different processes such as control of transactions, connections, and diagnostics. With this, the database server processes the inputted command and responds to the user’s command.'+article.headline +''; listEl.prepend('
WASHINGTON — The Mueller Report confirms the Russians tried to hack the Illinois Board of Elections website in 2016.
“In one instance in approximately June 2016, the GRU compromised the computer network of the Illinois situation Board of Elections by exploiting a vulnerability in the SBOE’s website. The GRU then gained access to a database containing information on millions of registered Illinois voters and extracted data related to thousands of U.S. voters before the malicious activity was identified,” the report states.
This was partake of an endeavor of the Russian intelligence agency — the GRU — to determine “vulnerabilities” on websites of more than two dozen states, including Illinois.
READ IT HERE: Redacted version of special counsel’s report
The Chicago Sun-Times reported on the hacking attempt in 2017.
The hack had nothing to attain with counting votes in elections in Illinois. The hackers looked at voting registration data: name, address, date of birth, gender and the final four digits in the social Security number. In all, hackers searched through about 80,000 records, with the elections board confirming the records of just under 3,000 voters were viewed by the hackers.
A report on the trespass prepared by the Illinois situation Board of Elections on Aug. 26, 2016, and obtained by the Sun-Times, details how the hackers were detected by situation board information technology staffers.
“Processor usage had spiked to 100% with no explanation,” said the report, with an analysis of server logs showing the “heavy load” was “malicious in nature,” and aimed at the online voter application website.
The particular form of the Illinois cyberattack was a “SQL injection” — as in Structured Query Language — where malicious code can subsist planted.
“We saw data being downloaded, and it was going to a status where it shouldn’t believe been going, so they shut them down,” Ken Menzel, general counsel for the situation elections board, told the Sun-Times in 2017.
“We had something that wasn’t properly battened down in one of the fields on the on-line portion of the website,” Menzel said then. That was “a mistake on their end.”
The data was going to a server in the Netherlands — not one of the 109 divide election jurisdictions in the situation of Illinois.
Server logs showed the cyberattack began June 23, 2016, with malicious SQL inquiries. Once detected the attack was detected, the staff establish in code changes on July 12, 2016.
On July 13, the website was taken offline. On July 19, the Illinois general Assembly and the Illinois Attorney general were notified. The AG’s office notified the FBI, which started an investigation in cooperation with the Department of Homeland Security. On July 21, the site was coming back online.
Attackers continued to hit situation board IP addresses five times per second until Aug. 12, 2016, Menzel told the Sun-Times in 2017. That’s when attacks abruptly ceased.
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