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A2090-612 Accessment: DB2 10 DBA for z/OS Assessment

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IBM Accessment: DB2 10 DBA

IBM Launches DB2 10, InfoSphere Warehouse 10 for huge statistics | actual Questions and Pass4sure dumps

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Guardium Stands Tall Over DB2 on i Data | actual questions and Pass4sure dumps

You may own tough security policies and succeed IBM i security recommendations to the letter. But if you’re in a regulated industry, that’s not trustworthy enough–you necessity to actually prove you’re doing things right, and that means auditing. IBM i shops that puss these requirements may want to check out the latest release of IBM‘s Security Guardium software, which includes unique capabilities for IBM i.

Buried in IBM’s October 5 announcement for IBM i 7.1 Technology Refresh 11 and IBM i 7.2 TR3 were several snippets about Guardium and IBM i’s relationship to it. The unique TRs mesh with the unique features IBM delivered with Guardium 10, which shipped on August 28.

“Do you know what’s going on on your system,” asked IBM i product manager Tim Rowe in the October 5 webcast announcing the TRs. “Do you necessity to own an audit trail? They know they own security folks who toil on a machine and they’re people who they truly trust. But when it comes to security compliance, you necessity to own audit capabilities. You necessity to own proof that even the trusted folks are doing the perquisite things.

“That’s where Guardium comes into play,” he added. “Guardium has a number of cold updates that were added to version 10.”

Guardium got its own slither in the October 5 TR webcast.

For starters, the Guardium Vulnerability Assessment (VA) component in version 10 now works with IBM i. IBM i shops can now consume the VA utensil to scan their databases to detect vulnerabilities, such as missing security and HIPER PTFs, infirm passwords, unauthorized changes, misconfigured privileges, and other vulnerabilities. It will also recommend changes if it detects problems.

In addition to DB2 for i, the VA features in Guardium 10 added uphold for analytical databases Teradata AsterDB and SAP HANA and the NoSQL database from Mongo DB.

The Guardium Database Activity Monitor (DAM) utensil has been enhanced to toil more effectively with IBM i and its integrated DB2 database, IBM says. This includes improvements to audit server filtering and the unique capability to consume equal and not-equal operators. Administrators can also set up DAM to scan against multiple policies, enabling it to “capture complete activity details without excess auditing noise,” IBM says in the announcement.

This release also gains enterprise high-availability features relish fail-over over and load balancing, Rowe says. “Guardium has been supported on IBM i for a while now,” he continued. “We continue to toil with them very closely to relieve drive the requirements for security for DB2 for i … up to the product so this works . . . as well with the other DB2 families as it does for IBM i.”

IBM also changed the appellation of the product with this release, which shouldn’t dumbfound anybody. The primitive appellation was IBM InfoSphere Guardium, while the unique appellation is IBM Security Guardium. For more info, see IBM’s October 5 TR announcement note or IBM’s August 25 Guardium v10 announcement letter.


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Enterprise AI and machine learning: Comparing the companies and applications | actual questions and Pass4sure dumps

If you've ever had the delectation -- and they consume that word lightly -- of pricing cloud computing services, you'll exist delighted to know there's a total unique roster of offerings to complicate your buying decision, under the rubric of simulated intelligence (AI).

Also: Automation technologies, AI, and robotics are faultfinding CIO targets

The colossal Four cloud computing majors -- Amazon, Microsoft, Google, and IBM -- complete present the capacity to construct and sprint neural networks and other forms of AI in their public cloud computing facilities, and they complete own various tools and various prices for doing it. Yet another class of services are provided by the cloud SaaS champs, Oracle and Salesforce.

There are so many choices, with so many idiosyncrasies in their features and pricing, that you might necessity some simulated intelligence just to figure out which are the best deals.

Fortunately, ZDNet is offering actual intelligence: We've studied the various offerings and compiled ways to consider about the buying decision.

The trustworthy news: There's a lot of overlap in the services, and there are many ways to regain started for free. You own choice, and you can start out by dipping a toe in the water.

The less-good news: Your final conclusion will depend on a mindful assessment of what your goal is in a noiseless very nascent realm -- machine learning (ML). You may not know until you spend some time working with these vendors' technology just what exactly you want from their services.

Makers versus takers

The first thing to carry out is to consider about yourself and your company in relation to these offerings.

Also: Making sense of Microsoft's approach to AI 

Machine learning lets a company find patterns in data. That simple statement encompasses a wide variety of goals, from detecting sentiment in a text document to projecting the next action to boost with a customer based on a history of interactions.

To understand that spectrum from a practical standpoint, consider of yourself in one of two buckets: Makers and takers.

Makers are those who wish to build some potentially unique application, perhaps from scratch, or at least with a heavy degree of customization -- from preparing data, to designing the neural network model that will exist used, to how it will exist served up. That can involve a lot of experiment with areas of data science and machine learning concepts at the very bleeding edge of the discipline, and revising one's toil over many hours in computing time. A maker is one fragment data scientist, one fragment IT administrator, and one fragment business analyst -- or perhaps a team comprising complete those abilities.

A taker, on the other hand, is someone who wants to quickly consume some benevolent of AI capability with a minimal effort. A taker may exist a marketing exec or sales rep with no scholarship of AI, or an IT admin who simply wants to deliver unique capabilities to customers or employees who own to consume those applications.

Thinking about the two uses cases immediately begins easing the buying decision.

Must read Those who originate AI

Makers build neural networks, train them, and then unleash them on real-time signals, which could exist batches of transactional data or individual transactions via a web commerce site.

Also: Mind the gap: AI and machine learning lag in adoption

That requires preparing data, designing a model to test against some data repository, training it on a great set of data, and finally deploying it as a live service.

That means purchasing storage -- for progress data, training data, and for the data returned as a result of a query using the live, trained model.

The process with each of the colossal Four starts by setting up a cloud account and choosing a storage option. This stage already involves choices -- not just about how much data, but how you're going to dissect that data in your neural network. Google, for example, offers two kinds of pipelines for machine learning data, called Dataproc and Dataflow. Dataproc is optimized for using the Hadoop file system with analysis packages that are meant to wield it, such as Spark ML. Each has different per-gigabyte pricing plans. Dataflow is meant to ingest either batch or stream data via things such as Apache Beam. It is meant to exist used for Google's Machine Learning Engine, where one builds models with TensorFlow or PyTorch, or another ML programming framework.

The point is, putting complete your data in public cloud is a colossal buying conclusion in itself. Unless you've already standardized on Amazon's S3 storage, or Microsoft's Azure Blob storage, you may want to first try out the options with a free account from a vendor, and monitor what benevolent of economics you'll achieve as you travel along. complete the vendors present free accounts for just this purpose, and most of those free offerings will ultimate up to a year, so you own some time to explore.

Plethora of choices

Once you've got the data, you own a plethora of choices for making things. The simplest and most springy option is the various machine learning engines with which you can build multiple models in TensorFlow and other frameworks. These are Google's Cloud Machine Learning Engine, Amazon AWS's SageMaker, IBM's Watson Machine Learning, and Microsoft's Azure Machine Learning Service. complete of them will let you purchase by the training hour, when developing the model, and then deploy based on a number of transactions. You own the greatest license with these offerings to bring in different frameworks in which to program models, and to pick the configuration of machine, such as reminiscence and processor cores.

Also: Sensor'd Enterprise: IoT, ML, and colossal data

At this point, you may also want to consider options for accelerating the assignment of training or performing inference. Google, of course, makes a play for its Tensor Processing Unit, a custom chip now on its third iteration, that is expressly designed to accelerate the matrix math at the heart of training models. Microsoft promotes consume of field-programmable gate arrays, or FPGAs, called Project Brainwave. Amazon, in addition to developing its own chips for running model training, has announced a chip called Inferentia, which will exist available sometime later this year. complete four present graphics processing units, or GPUs, which own become the workhorse of model training, to accelerate workloads.

There are several ways to simplify your setup, and the buying process. They comprise prepackaged virtual machines and containers designed specifically for machine learning and data science. Google offers the Cloud abysmal Learning Virtual Machine, Microsoft offers its Data Science Virtual Machine, and Amazon has the abysmal Learning Amazon Machine Image. IBM takes a degree different tack, promoting its Watson abysmal Learning Studio as a dedicated program that can exist used to visually drag and drop components of a machine learning model. Microsoft has something similar with its Machine Learning Studio.

A key distinguishing factor for both Microsoft and IBM in complete of this is their capacity to wield on-premises machine learning. With abysmal hooks into decades of enterprise wares, the two vendors present more substantial offerings for companies that want to discharge machine learning on their own infrastructure. IBM's Watson Studio can exist used behind the firewall to build and train models, which can then either exist deployed in the cloud, or deployed to the local data center with the option of Watson Machine Learning for Private Cloud. Another option is IBM's Watson AI Accelerator, a software stack running on the company's Power line of servers on premise. IBM advises this for building out large-scale deployment of heavy deep-learning AI models.

Similarly, Microsoft's Azure ML Studio can exist used behind the firewall to design neural networks, drawing training data from the company's SQL Server database. There is also a version of Azure Machine Learning that's a licensed server product for on-premises deployment. Analytics functions can exist constructed natively in SQL Server. And even the public cloud version of Azure Machine Learning can draw data from the on-premises SQL Server. Clearly, there is a plethora of private and hybrid functions.

In both IBM and Microsoft's case, a strong controversy for on-premises is that the biggest consume of data is during the training age of a unique neural network. If customers can carry out that toil in their own data centers, they stand to save a bundle on buying storage in the public cloud.

Whichever vendor you travel with, you'll want to scrutinize the programming frameworks and tools each one offers. complete the colossal Four  uphold the most well-liked AI frameworks, TensorFlow, and PyTorch. Amazon and Google mind to uphold a greater breadth, including Sci-kit Learn, MXNet, Rapids, Spark ML, and XGBoost. There are some that own become dividing lines, such as the ONNX framework to establish a common framework between models, supported by Microsoft and Amazon, but not Google. IBM has its own package for data analysis forms of machine learning that's unique to it -- SPSS Modeler. You'll own to double check if your favorite framework is supported.

All of the services, in addition to offering special workbenches such as Watson Studio, allow you to consume well-liked tools for prototyping neural networks such as Jupyter notebooks or Pandas. Your biggest question as you test these services is how easily you can straggle data and models in and out of the repose of the cloud workflow.

Taking AI on a consumption basis

Let's puss it: A lot of people talk about AI when complete they really want is to discharge some simple data analysis without conducting fundamental data science. For those who would rather skip a lot of coding, there are a growing number of APIs that can exist plugged into an app, or prepackaged solutions that deliver a ready duty such as understanding natural language or running a chat bot.

Also: IBM takes on Alzheimer's disease with machine learning 

More and more, vendors are lamentable to unique ways to simplify building things. Google offers AutoML, which basically gives you the model for image processing (face recognition and demur recognition), natural language processing, and language translation. This means you can skip a lot of the toil of building a neural net from scratch. IBM later this year will release as a beta something similar, called Neural Network Synthesis, or NeuNetS.

In a similar vein, Amazon offers a raft of AI/ML services that comprise Comprehend, which identifies phrases, names of people and places, or brands, in text documents, among other things; Rokognition, which identifies people and objects in images, and can spot inappropriate content; and Forecast, which makes predictions when fed historical data by combining time string analysis with other data, such as product information, using machine learning.

Like Google's AutoML, Amazon's AI/ML services let you forego specifying a neural network model; simply sprint a script and the system tries a bunch of nets and you let it know when it arrives at predictions that fulfill your objective. APIs let you incorporate the results of predictions into your applications.

Microsoft offers Azure Cognitive Services, including vision, language and speech services, to classify images, understand spoken phrases, and create question-and-answer sessions from documents such as an FAQ.

IBM's Watson offers a raft of services within categories such as scholarship and Data and Speech that offers functions such as text-to-speech, speech-to-text, and the scholarship Catalog, which can find, curate, and categorize data within meta-data you feed it.

In each of these cases, you not only don't program, you don't own to provision infrastructure services from the major vendors. You simply set up your data in the cloud and pay by the amount of characters or documents or images you want, in varying rates from each vendor.

Many of these APIs are an extension of the belief of serverless computing, where programming functions can relate many different functions together. Hence, each vendor's cloud serverless functions can exist used as glue to tie together these AI and ML services. They comprise Amazon AWS's Lambda architecture, Microsoft's Azure Functions, Google's Cloud Functions, and IBM Cloud Functions. For takers of AI, serverless functions will exist an increasingly distinguished glue to stitch together lots of capabilities rather than writing everything from scratch.

More and more, the vendors are adding functions that originate these basic machine learning tasks behave relish finished applications. Discovery News, for example, can dissect blogs and intelligence reports for categories and sentiments. Google is relatively unique with packaged offers, having recently rolled out Contact center AI, a muster handling app that uses virtual agent technology, and Cloud Talent Solution, a job search program.

The future is embedded AI

The next step for makers and takers alike is to incorporate AI into much larger applications. Known as embedded machine learning and AI, such programs are especially well represented by two giants of enterprise applications: Oracle and Salesforce.

Also: How to Implement AI and Machine Learning

Oracle has a solid pitch for makers who want to start from their data repository and toil outward from there. Its Platform-as-a-Service, or PaaS tools such as the Autonomous Data Warehouse and the Data Science Cloud are data stores that embed the capacity to develop and train neural network models, using TensorFlow and Sci-kit Learn and other well-liked frameworks.

For those who are makers, Oracle offers a suite of what are known as Adaptive Intelligence applications, in the domains of customer experience, enterprise resource planning, and manufacturing. These applications act as add-ons, for a divorce fee, that integrate with Oracle's traditional apps in those areas. Models built by Oracle will succumb insights such as a next best action for a sales team, or how to provide optimal discounts to suppliers. Oracle enhances the offering with what it calls 'Firmagraphics' -- data on companies and industries that the company has amassed through a number of acquisitions.

Salesforce stakes out a position firmly in the taker camp, with its Einstein family of machine learning functions meant to enhance its selling and marketing and customer service apps, similar to Oracle. Within an application for sales, for example, a rep will see lead scoring of prospects, based on an assemblage of neural network models that the company runs under the hood, as a tournament of competing machine learning.

The makers -- the Salesforce admins in a company amenable for providing the applications to enterprise users -- can deploy the capabilities without engaging in the design of models. Instead, they revolve on capabilities with the relieve of prompts from the programs that recommend features suitable to the organization, which can exist customized to the firm's needs.

Oh, the prices you'll calculate!

Have your calculators ready -- or, better yet, reach for an online bill calculator, because machine learning in the cloud involves a variety of pricing models that achieve a degree involved equation.

Also: The next step for machine learning and AI  TechRepublic

The colossal Four pricing plans for doing the most sophisticated AI progress and training are generally broken down into divorce training and inference pricing. Hours of training are then multiplied by various forms of units of capacity, to reflect the compute power you're using depending on the benevolent of compute instance you select.

There are exceptions. For example, IBM prices its Watson Machine Learning as a combined training and inference cost, degree reflecting the view that training may exist done offline, behind the firewall. Microsoft doesn't saturate for training, it says, although you noiseless own to pay for the underlying virtual machine instance.

Choosing acceleration chips, such as GPUs or Google's TPU, adds another cost on top of the groundwork price.

For some of the API choices, such as video search, image categorization, or text to speech, you'll pay in allotments of pennies or dollars per minute of video, or thousands of images, or thousands of characters of text, based on how frequently you are sending API requests to discharge inference.

Still other modules are on a per-seat basis. IBM charges $99 per user, per month, for the cloud version of its Studio neural network design, but $199 per month for a desktop version. Another fee is charged for local installations behind the firewall.

Oracle's Adaptive Intelligence apps range in charge for the different bundles but are charged based on a per-user license, with the CX version, for marketing, sales and services roles, costing $1,000 per month per user, plus $5 for every 1,000 interactions per month.

Salesforce applications are included with the Unlimited version of the company's Lightning platform, but for other cloud SKUs, there's an extra saturate of $4,000 per month that increases depending on the units of millions of predictions you quiz of the software.

Also: Turing Award honors pioneers of AI CNET

Remember that in several cases, customers will halt up amassing store of credits, such as in Oracle's system, which can then exist allotted to services on a case-by-case basis. Consequently, spending may exist a matter not merely of budget allocation but also deciding how to spend credit already collected with a given vendor.

Using the online calculators can exist helpful, but your best pot is to try the free version of each application. This way, you can regain a feel for how machine learning training time adds up, in the case of building or customizing machine learning models; how much data you'll own to consume in the cloud; and at what rate you're likely to draw predictions from any of these systems. Especially for the ultimate item, the meter is running once you travel live with an AI model, and will preserve running for as long as you and your users preserve asking the system for predictions.

The Offerings Google Cloud Platform Cloud Machine Learning

Google has arguably the deepest portfolio of machine learning technology of any of the colossal Four. You could carry out worse than consume the company's own developed algorithms in its AutoML service. And the Tensor Processing Unit chips are a unique offering for those in the market for AI acceleration. Google's control of the ubiquitous TensorFlow framework for machine learning implies you're in especially trustworthy hands if that's your progress platform of choice. 

Amazon AWS SageMaker

Amazon has been in the cloud computing business longer than anyone, so the breadth of offerings to complement SageMaker is substantial, and many may already exist confidential with pricing and buying in the Amazon system. The company's marketplace of third-party machine learning programs that can exist added on top of Amazon's own is superior to others. The introduction of custom ARM-based processors for cloud compute will exist complemented later this year by Amazon's first home-made inference chip.  

Microsoft Azure Machine Learning

As a pioneer in speech and vision and natural language processing, Microsoft's Redmond research labs own endowed the software giant with a substantial title to greatness in modern machine learning, which should inform the company's cloud AI offerings in those functions. Microsoft can also provide an on-premises or hybrid cloud machine learning sustain with enterprise applications such as SQL Server that embed analytics and machine learning capabilities. Its progress of the open gauge ONNX technology for AI model portability also sets the company apart, as does its progress of FPGAs as acceleration tools for machine learning inference.

IBM Watson Machine Learning

IBM has the richest set of tools to boost AI from a company's internal data sets complete the route to publicly accessible web services that deliver analysis. The company's Watson Studio acts as a hub that can coordinate the reaching into on-premises repositories such as Db2 or Oracle DB; antiseptic up and prepare the data via multiple programs such as Data Stage or Cloud Private Data; dissect it with applications such as scholarship Studio; and then deploy predictions to the web, complete built upon a modern Kubernetes architecture. IBM's decades of interaction with transaction processing systems means an added capacity to discharge machine learning on things such as fraud and regain a result within a window of milliseconds necessary for every transaction.

Oracle Adaptive Intelligence Apps

With decades in transaction processing and the database that stores the vast majority of enterprises' data, Oracle is well positioned to originate machine learning a duty within the very user interface that customers consume daily. The company has coupled its infrastructure-as-service offerings, such as bare metal computing, to its extensive developer platform in the cloud, as platform-as-a-service, to originate workable autonomous programs that hurry up database functions by anticipating much of the analytic toil that would own to exist done by hand. Programs such as the Autonomous Data Warehouse can then feed into the Applied Intelligence applications to deliver line-of-business predictions such as which customers are more likely to exist closed in a given time frame, or which suppliers should exist given special payment terms.

Salesforce Einstein

The Einstein suite from Salesforce offers the very simple, simple approach that the cloud company pioneered, a minimum of date with the messy details of provision and deploying software and systems. The focus is applications that sit atop the company's existing cloud-based commercial apps, making deploying and consuming machine learning as effortless as workable for admin and IT worker. No machine learning progress is required for an admin to revolve on functions, and predictions, such as the next best action for a sales rep, are surfaced in the context of the apps they already use. Salesforce can draw upon 20 years of customer trends as data that fuels the predictions of the embedded algorithms of Einstein.

Other Players

In addition to the colossal Four, a number of adolescent companies are offering overlays to cloud computing that point to hurry machine learning model training and deployment, and that in some cases can present lower rates on compute and storage by amortizing costs across many users.


Straight out of Brooklyn, unique York, the Intel-backed startup offers a job scheduler called Gradient that handles the details of running neural networks in the cloud. You install the company's command-line on your local machine, revolve on a Jupyter notebook, pre-packaged with machine learning frameworks, and runtimes complete in a Docker container that packages up your model, which is then submitted to Gradient to exist sprint in a cloud instance. You pay either by the hour, with rates varying by CPU, GPU, or TPU, or for a flat monthly fee of $8 for teams, with other rates for enterprise use. Data storage charges also apply. 


With an illustrious crew from Microsoft and Oracle, and backers such as YCombinator and Gitbhub, FloydHub aims to simplify model deployment via a simple command-line interface connecting to cloud computing instances, similar to Paperspace. The company offers monthly plans of $9 for individuals and $99 for teams, as well as the option for per-second pricing. 


Run by former Citrix Systems CEO impress Templeton, DigitalOcean claims it can regain your compute instance in the cloud up and running in as petite as 55 seconds, using pre-built virtual machines with selection of Linux distributions, called droplets. An API lets you start and sprint multiple droplets in parallel and tag each one to filter job instances. Prices start at less than a penny per hour and present a wide array of compute configurations. A cluster of Kubernetes application containers can exist had for $30 per month. 


The Baidu-backed startup promises to let you test thousands of different models on multiple cloud instances from the command line. Infrastructure costs range from 27 cents per hour up to $6, depending on GPU selection, with a terabyte or model and data storage for $23, plus extra fees for pro and enterprise tiers. The company cuts the fees of classic cloud jobs by storing persistent Jupyter notebook instances and repeatedly re-starting spot GPU or CPU instances after they quit running.


Designed to exist ultra-fast machine learning setup, a web-based dashboard starts you off with a blank project template or a Github template that lets you clone a Github instance. Click a button and you're up and running in a Jupyter notebook online. The service features only one instance at the moment, an Nvidia K80 GPU with 15GB of reminiscence attached to a four- core CPU and 50GB of space. Pricing starts at $10 per month for individuals and $49 for a professional plan.

SQL Server Roundup | actual questions and Pass4sure dumps

A lot of wee releases were made by Microsoft’s SQL Server team ultimate month. Some of the highlights comprise Power BI for on-site servers, System center uphold for SQL Server 2014, and updated Java/PHP drivers. But first, Microsoft migration utensil for DB2 users.

SQL Server Migration coadjutant for DB2

Microsoft’s SQL Server Migration coadjutant for DB2 recently hit version 6. This utensil claims to “automates complete aspects of migration including migration assessment analysis, schema and SQL statement conversion and data migration”.

SSMA for DB2 v6.0 is designed to uphold migration from DB2 on z/OS versions 9.0 and 10.0 and DB2 on Linux/Unix/Windows (LUW) versions 9.8 and 10.1 to SQL Server 2012, SQL Server 2014 and Azure SQL DB.

Since DB2 offers features not available in SQL Server, this utensil also adds additional functionality to SQL Server to relieve it emulate DB2. Unfortunately, the specifics of this functionality doesn’t materialize to exist documented anywhere.

SQL Server Migration coadjutant is also available for MySQL, Oracle, Sybase, and Access. You can find more information on migrating from these database engines by reading the SSMA Team Blog.

Power BI

Power BI is ill defined, but in broad terms it is a set of business intelligence tools that are offered through outstrip and Office 365. In addition to the classic charts and reports, it includes a natural language query engine designed to allow non-technical users to quiz questions about the data.

The newly released Power BI Analysis Services Connector allows you to connect Power BI to an on-site instance of SQL Server Analysis Services. Usage of this connector requires first setting up dynamic Directory Sync between your Azure and on-site dynamic Directories.

System center Operations Manager

Users of System center Operations Manager can now “discover and monitor” SQL Server 2014 instances using the Management Pack for SQL Server 2014. This management pack is only for SQL Server 2014, a divorce pack is necessary for SQL Server 2005 thru 2012.

New features

  • Added Mirroring monitoring scenarios for SQL Server 2014 product
  • SPN monitor now has ‘search scope’ parameter which allows the halt user to pick between LDAP and Global Catalog
  • Fixed Timeout error in CPU utilization monitoring scenario
  • Monitoring SQL Server Instances on the very server with their own network interfaces and default port is now available
  • SQL Server instances with underscores and other allowed special symbols in names can exist monitored
  • Improvements

  • Dependency on SQL Server Library management pack has been eliminated.
  • Support of Localized performance counter for CPU and analytic Disk Metrics.
  • SQL Server Integration Services monitoring seed discovery.
  • SQL Server Integration Services moved to divorce management pack.
  • SQL Server non-default port is now supported.
  • Optimized performance collection replaced with raw performance collection to guarantee accuracy of hourly and daily aggregated data.
  • PHP uphold for SQL Server

    Microsoft is now present three versions of its PHP drivers for SQL Server.

  • Version 3.1 requires PHP 5.4.32 or PHP 5.5.16+ and SQL Server 2008 or later.
  • Version 3.0 requires PHP 5.3.0+ (5.3.6+ is recommended).
  • Version 2.0 requires PHP 5.2.4+ (5.2.13+ is recommended) and SQL Server 2005 or later. This version does not toil with PHP 5.4.
  • Java uphold for SQL Server

    Like PHP, Microsoft is offering multiple versions of its JDBC drivers for SQL Server.

  • Version 4.1 supports SQL Server 2008 or later and Java progress Kits 5.0, 6.0, or 7.0.
  • Version 4.0 supports SQL Server 2005 or later and Java progress Kits 5.0 or 6.0.
  • Both versions are ilk 4 JDBC drivers, which means they are simple Java drivers.

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