Talking Data Podcast » Episodes

In this end-of-the-year edition of the Talking Data podcast, Senior Executive Editor Ed Scannell joined me to speak with Mike Matchett, founder and principal analyst of the Small World Big Data consultancy, as we rambled through some of the signal events of big data in 2018. Mergers and acquisitions, naturally, tend to be the stepping stones when you look back at the path just traveled. Cloudera and Hortonworks, IBM and Red Hat – these deals set the tone for our end-of-year big data ruminations. But what rises in importance in our podcasters’ ponderings, are not the mergers in their dollar terms but instead the mergers as they reveal the underlying currents and eddies of telling trends. What surfaces? *Hadoop-centric big data analytics is morphing into machine learning and deep learning analytics. *It is not that the shortcomings of Hadoop data processing have been solved, however. *Rather, the vendors have declared victory, and moved on to the next world to conquer – the more mysterious one of AI, machine learning and statistics safely beyond the layperson’s ken. It’s happened before. *AI is what you do with big data. The Web and cloud have become irresistible honeypots for said data, and the result is that the balance of power – for data, IT and business — is moving to the cloud. *A long view would say that it’s taken more than 10 years for cloud computing to become an overnight success, and that assorted after effects will play out for some time to come. In the podcast we talk about the pendulum effect, of which 1990s client/server computing is a ready example. In that case there was a swing away from central IT, which was called “the glass house.” The era saw independent departments within businesses beginning to set their own technology courses. We see that with cloud today. A pendulum swing has put more technology decision making in the hands of developers within lines of business. They can use credit cards to start projects, and they can get very high-end systems via top cloud providers. We may look back one day and see things shifting back toward central IT. If so, it will no more resemble today’s central IT than today’s resembles the IT shop of the glass house days. The recent years have been topsy-turvy – and not just on the big data front. Thanks for hanging with us on the Talking Data podcast. – Jack Vaughan The post Currents – Talking Data End-of-Year Podcast 2018 appeared first on Talking Data Podcast » Episodes.

As some TechTarget reporters were finishing their last podcasts for the year, we sat down briefly and tried to view the longer picture, to look through the glass darkly toward the past. Now, you are taught not to dwell on history from your first days in this field called journalism; people can buy books if that is what they want. But a calendar with days rapidly dwindling might lead you to do just that, and best editorial practices be dammed. And the tentative conclusion on some of our parts was that the big mergers of 2018 don’t stack up to those of yore. That is even though the transactions hit some pretty heady dollar amounts. They don’t really seem on par with the big mergers of the past 20 years, these jaundiced observers ventured. IBM buying Lotus, Oracle buying Sun — those were some game changers. They signaled big industry shifts or put bookends on identifiable tech eras. Maybe it was the outsized nature of some of the characters involved. An ongoing move to cloud computing is behind IBM’s bid for Red Hat, or Microsoft’s deal for GitHub, which are the topics discussed in this podcast. The move to cloud now seems predestined, but how it will actually transpire for these noted player will be determined by customers. Stay tuned. This is part of a series of end-of-the year podcasts. I joined Senior Executive Editor Ed Scannell and analyst Mike Matchett, principal and founder of the Big Data Small World consultancy, for this  SearchDataManagement-hosted look-back at 2018 podcast. The post Both IBM and Microsoft get open source religion in big 2018 acquisitions appeared first on Talking Data Podcast » Episodes.

New apps for cloud have found a home on Azure’s cloud database. What about existing apps? On closer inspection it appears that there is work ahead. At PASS 2018, Craig Stedman encountered signs of progress therein. Kicking off the event was Microsoft’s database group leader Roland Kumar who, Stedman reports, discussed managed instances of SQL Server on the cloud that more functionally equivalate with downhome SQL Server on premises. In any case, the pace is quick. Check out the latest Talking Data Podcast and related SearchSQLServer coverage for all things PASS. – Jack Vaughan The post More things PASS appeared first on Talking Data Podcast » Episodes.

This podcast considers how likely it is for existing users of Oracle and Microsoft to move to the cloud, as well as what obstacles they may face if they make the leap. Senior Executive Editor Craig Stedman tells us that’s still a work somewhat in progress. And, I get a chance to provide a take on Oracle’s comparable moves, hearkening again to my days at Oracle Open World in October.  Download the podcast and learn as we compare notes from our recent travels. Be there when “worlds collide.” – Jack Vaughan The post Looking at Oracle and Microsoft and recent showcase events appeared first on Talking Data Podcast » Episodes.

Last month we ventured West to cover Oracle Open World in San Francisco. Now, in a Talking Data Podcast edition recorded live on tape from San Francisco’s Moscone Center, intrepid reporters Jack Vaughan and David Essex discuss what they saw. Some of it was familiar – as always, Oracle’s Larry Ellison delivered a notable keynote. Some of it was new – Ellison’s discussion was much about cyber trust, impenetrable barriers and the gremlins lurking in the cloud. The post Larry Ellison’s cloud-borne cyberthreats and bots, oh my! appeared first on Talking Data Podcast » Episodes.

Remember when the Web first caught on? One thing I remember is people saying “yeah, it is pretty cool, but, you know, it is stateless.” As most of what I heard on this issue was from enterprise software vendors, with all the bias that could entail, I should have taken what I was told with a grain of salt. The first big problem these folks saw with the Web was its statelessness, which made it far different from the synchronously connect clients and servers (at that time, Java servers) they were used to. The first problem the Web presented, as the enterprise crew saw it was to connect the Web to the database, which – no question – was a transactional relational database. The first response to the problem was CGI, which quickly faded, but which, alas, still shows up in a URL address window every once and a while. There soon followed JMS message queues, Web Services and SOA, and then REST and AJAX. Then the Web turned into the Cloud and NoSQL, Kafka, container-based microservices and Kubernetes orchestration hit the beach. These thoughts were like an elusive butterfly in my mind as I ventured recently by Amtrak to cover the Strata Data Conference at the Jacob Javits Center on the Hudson River in New York City. Kafka certainly did seem to be a common theme in the schema presenters displayed in the technical sessions, and the Kubernetes sessions were overflow. At the event I had a chance to speak with Jay Kreps, CEO and co-founder of Confluent, and one of the creators of the aforementioned Kafka publish-and-subscribe messaging bus. He told me that event processing is gaining a commanding presence on the scene. You could say it’s “what’s happening.” All along, the general trend toward event-based microservices architectures has overlapped with new distributed data architectures, which are edging toward becoming mainstream competitors to traditional enterprise data warehouses. You could think of Kubernetes as the other key part of the middleware replacement story that is going on today, he said. At the same time, he added, “Nothing in the enterprise goes away overnight.” There is more from my conversation with Krebs in the latest episode of the Talking Data Podcast, including a conversation on the new architecture with expert analyst Mike Matchett of Small World Big Data and the ruminations of the author as the train to New York drew near Penn Station for Strata. – Jack Vaughan The post Web, what have you wrought? On Strata, microservices and more appeared first on Talking Data Podcast » Episodes.

In this episode of the Talking Data Podcast we are joined by Nicole Laskowski, senior news writer for SearchCIO.com. She tells us about a podcast series she and her colleagues have created known as Schooled in AI. This series looks at cutting-edge AI research being done at Carnegie Mellon University in order to give IT leaders in businesses a clear view on where things are headed. This discussion is preceded by Laskowki’s comments on a recent O’Reilly survey that appears to show – in what may be a surprise to many — that business folk are quite aware of the bias that AI can bring out as it reaches data-driven conclusions. Listen to this Talking Data episode, and be sure to visit Schooled in AI as well. – Jack Vaughan The post Back to school time – Learning and AI at CMU appeared first on Talking Data Podcast » Episodes.

For this episode of the Talking Data podcast, Mark Labbe takes a look at the MIT Startup Exchange. This program gives members of the MIT community a chance to show their wares, and as you may have guessed, those wares these days have a lot to do with AI and machine learning. Among the underlying trends, Labbe tells us, are natural language processing and geo-location. Labbe came across the MIT startup activity as part of his coverage of the recent Forrester AI Forum in Boston. Also discussed,the work of an IBM machine learning system versed in debate strategy and tactics. Listen to Talking Data and get a feel for where AI may be headed. The post Start-ups ply the AI trade – language and location are emerging themes appeared first on Talking Data Podcast » Episodes.

At times the era of big data has taken on the flavor of the old West – the kind depicted in a movie like The Treasure of the Sierra Madre. While it was seldom an outright confrontation, there’s little question that conscientious data stewards were usurped in some organization by developers who slightly resembled freewheeling bandits such as those you didn’t “need no badges” as they went about their business in John Houston’s film. We are a few years into this, and now there are signs that a bit of taming is going on in the Hadoop ecosphere. The recent approaches for bringing Hadoop-style data processing into wider production seem to bespeak a change. The General Data Protection Directive (GDPR) is in some part a driver of that change. At last month’s DataWorks Summit in San Jose, California we spoke with Constellation Research analyst Doug Henschen, who agreed the shift of enterprises to include large-scale open-source distributed data processing in their analytics arsenal is now tempered by increased interest in data governance. “What you see is companies re-platforming – that the buzz,” Henschen said in this episode of the Talking Data podcast. “Companies understand that they need a sort of next-generation information architecture.” We have seen attempts before to add tooling to data lakes to tag and curate data, but now the push may be more fevered, and GDPR may be the impetus. “The push for GDPR has gotten people thinking more and more about the governance aspects of that,” Henschen said. “As they are re-platforming they have an increased eye toward data governance, data lineage, access control, security — all of these good things that we have long required but haven’t necessarily nailed.” Catch up with the big data doings in this edition of Talking Data. – Jack Vaughan The post Governance is coming to next-generation information architecture appeared first on Talking Data Podcast » Episodes.

Real estate listing firm Trulia is on the cutting edge of applying computer vision. In this edition of the Talking Data podcast, we talk with the company’s vice president of engineering, Deep Varma, to learn more about how his team is applying computer vision. As you’d expect, Trulia’s computer vision processes are built around deep learning algorithms. These are the machine learning models powering most of today’s most advanced AI applications. And while engineers have made significant advances in the functionality of these models, impactful business applications have been slower to come around. Trulia, however, has found an interesting way to implement image recognition deep learning models to build computer vision applications that are capable of identifying and describing specific objects in images. This gives prospective home buyers an idea of what they should expect from a listing before clicking on it and enables Trulia’s recommendation engines to surface more relevant results. Listen to the podcast to learn more about how the company is deploying deep learning models in the area of computer vision. The post How Trulia deploys computer vision appeared first on Talking Data Podcast » Episodes.

The trend that sees the SQL query engine appearing on Hadoop, is just the start of a movement; the SQL query engine running on data other than HDFS may follow. If these trends portend fitful change for users, they also affect vendors. One vendor’s journey here is particularly telling. Starburst Data might be called a ‘re-start-up.’ The company was the brainchild of some young data technicians that included Daniel Abadi, an academic researcher who helped forward the notion of column-store parallel databases in the early 2000s. In 2011, he helped form Hadapt — one of the first Hadoop-on-SQL providers. In 2014, the company was purchased by Teradata. The timing proved a bit odd, as it nearly coincided with Facebook ceding much development responsibility to Teradata for Presto, a SQL-on-Hadoop tool that the social media giant had forged in-house, and which has subsequently been endorsed by no less than Amazon for its Athena SQL engine. The former-Hadapt group within Teradata shifted its efforts to improved performance for a Presto-compatible SQL query engine. At the end of 2017, Hadapt principals within Teradata spun-out to form Starburst, with Teradata’s blessings. A Starburst goal is to bring SQL engine prowess to SMBs that are still outliers in Teradata’s more familiar big player universe. An early effort for standalone Starburst has been a Cost Based Optimizer for Presto, built in collaboration with Facebook technicians. For the many lovers of SQL joins, the new optimizer supports Join Reordering and Join Distribution Choice. The picture emerging shows differences in use cases between plain vanilla Hadoop and SQL on Hadoop – the difference is between Hadoop being fit for the purposes of small data science groups and skunk works to Hadoop being useful for the interactive needs of wider groups of SQL business analytics users. We are also seeing HDFS, the file system at the base of Hadoop, giving way as more people choose to pursue these types of applications on the cloud rather than on the premises. Listen to the latest Talking Data podcast, which features Starburst Data CEO Justin Borgman. We left a noisy restaurant to record the interview, and found a noisy Boston waterfront, with massively loud construction if full throat. Enjoy! – Jack Vaughan The post SQL query engine – Presto change-o Starburst appeared first on Talking Data Podcast » Episodes.

It’s been said Oracle leader Larry Ellison advises his troops to focus on one competitor at a time, and in recent years that has been Amazon. What started out as an online book store eventually morphed into a general mega-store, and then, surprisingly, a mega-IT-outsourcer. In many ways it created the cloud computing formula. Like other leading lights of enterprise computing, Oracle is in the midst of efforts to shift focus from customers’ on-premises data centers to its own cloud computing centers, and to keep those customers in the Oracle camp. Oracle’s counter thrusts to Amazon are one of the defining aspects of technology today. But it is a balancing act. The Collaborate 2018 conference at Mandalay Resort and Casino in Las Vegas would seem an apt place to take measure of Oracle’s progress toward cloud. Recorded as the event began, this remote edition of the Talking Data podcast sorts through challenges the Redwood City Calif. -based IT giant faces on the road to cloud. Underlying its cloud efforts are moves in both databases and applications. Those are key columns of Collaborate, which brings together IOUG Oracle database users, OAUG Oracle eBusiness applications users and Quest JD Edwards/PeopleSoft applications users. Steady cloud movement, but less than a startling shift, seemed to be the basic cloud status takeaway from the event. These databases and applications suites are well entrenched in organizations, usually in very large enterprises. Moving these into cloud is a multiyear project in most cases. While complex enterprise applications stay home, new applications are driving to the cloud. A state of steady cloud movement — but less than a startling shift — seemed to be borne out at Collaborate 2018. At the event, we asked: “How is the Oracle database and applications cloud migration going?” “We are not seeing enough large scale movement to really tell . It’s really just one-off stuff,” Stephen Kost, CTO at Integrigy, a security software and services provider, told this reporter at the conference. “People are moving to small web applications.” In Kost’s view, much of Oracle’s strength is in large companies that may have 1,000 databases – but he has seen, in many cases, only a handful of those have yet to be moved to the cloud. Up the Las Vegas strip from Collaborate this same week, perhaps not so coincidentally, another Oracle-related conference took place. NetSuite SuiteWorld 2018 was built around the cloud ERP offerings that became part of the Oracle portfolio via acquisition in 2016.  As at Collaborate, much of the discussion was around embedding AI into applications. Oracle’s purchase of NetSuite was a tacit admission that “cloud is different” and that it needed a wholly separate product line to attract small- and medium-size business customers to its applications. It was also an admission that it saw cloud migration as a multi-year effort that needed to be addressed from several directions. In a phone call after Suite World, Holger Mueller, Constellation Research, told us Oracle has avoided the temptation to roll NetSuite together with its incumbent applications suites. At the same time, he said, it has been expanding NetSuite globally, and injecting elements of its AI and machine learning research and development. That is also what Oracle has begun to do with the e-Business Suite, JD Edwards, and PeopleSoft portfolio. Still, for now, Oracle’s cloud application migration might be described as a delicate balancing act within a delicate balancing act.- Jack Vaughan Click here to see a video version of this podcast. The post Oracle’s cloud plans for applications of the enterprise kind appeared first on Talking Data Podcast » Episodes.

Recent convocations of the Strata big data conference have seen a move away from sessions focused on data infrastructure and Hadoop and toward analytical applications and data science tools. Where is Strata going? Strata, it seems, cannot contain itself, when it comes to software containers. News around Kubernetes and containers figured prominently in coverage at the recent Strata Data Conference in San Jose, Calif. Containerized apps have significant benefits – so big data developers are discovering. Big data applications that sprouted up indiscriminately in organizations are now being folded into central data lakes and the like, and containers are increasingly seen as a flexible way to handle a procession of distinct and ever changing analytics jobs. Putting jobs in self-contained units seems a way to bring order to the workloads. A key appears to be container orchestration, which, in the form of the Kubernetes open source standard, seems poised to usher in a new way of handling big data work. That is according to TechTarget Senior Executive Editor Craig Stedman, speaking with Senior New Writer Jack Vaughan. Among vendors that showcased their container-related efforts at the conference are such as Blue Data, data Artisans, Intel, MapR Technologies, Pepperdata and others. They almost universally cite recommendation engines, machine learning, and AI as apt first targets. Still, a message that arises in this podcast is that it is early. While improved speed of development and deployment seems in the offing, a lot of preliminary work must be done before pushing that magic button of container automation. Containers may be one of those unique cases where software engineering takes significant inspiration from work in other industries. That is because the container metaphor comes straight from logistics advances that, beginning in World War II, transformed global shipping. In the world today, 90% of non-bulk cargo is convey by containers – perhaps, someday, something similar will be said about software containers for persistent data. There is much that needs to be developed when it comes to persistent containers for big data, but important efforts are now underway, as discussed in this edition of the Talking Data podcast. Listen, and learn more. – Jack Vaughan The post Big data containers set sail appeared first on Talking Data Podcast » Episodes.

C-suite folks and others have taken notice this week as Facebook finds itself in a sack of woe. Data privacy is at issue. The Silicon Valley high flyer has gained the kind of publicity you don’t want, in the wake of news that its social media platform was used to gather up Facebook profile information of thousands (or millions — the full details of the story are still coming in) of users for political consultancy Cambridge Analytica for still to be determined purposes. Cambridge Analytica did its gathering via a third-party that fielded a “thisisyourditigallife” survey app that walked users through personality quizzes and, incidentally, scraped info from their and their friend’s profiles. Facebook in its responses to this news at first stridently held this was not actually a data breach, but was instead a rogue developers’ wandering from company data policy. Let’s call it a “data faux pas of the developer kind.” In the face of general uproar, that somewhat dismissive response was somewhat dialed back. Which brings us to GDPR. Facebook’s latest egg-on-its-face moment comes only a month after COO Sheryl Sandberg had assured a Brussels, Belgium crowd that the company was ready for the General Data Protection Regulation, or GDPR, which is the EU’s comprehensive data privacy edict, due to go into enforcement in late May. GDPR is intended to give web users more control over their on-line data profiles – that is just the type of control that seems now to have been lacking in the Facebook-Cambridge Analytica episode. Data professionals have been grappling of late with GDPR which, by some measures, is meant to bring about a firm style of data governance — one that might have gone missing during the big data gold rush of which Facebook was a big part. How ready data professionals are for GDPR is a matter of conjecture, and it is the topic of this edition of the Talking Data podcast, recorded after Sandberg’s Brussels pronouncement, but just before the news on Facebook and Cambridge Analytica broke. The podcasters discuss GDPR, and the different uptake rates it’s likely to exhibit in different industries and departments within companies. Marketing, they suggest, may be a little late to the GDPR party but starting to get involved. Whether you call the Facebook-Cambridge Analytica event a data breach, a data faux pas, or what have you, it is fair to say the latest doings will quickly bring data privacy and GDPR to more people’s attention. Many viewers expect Facebook will be among the first to hear the GDPR enforcers knocking. – Jack Vaughan The post Facebook data privacy missteps seen in light of GDPR appeared first on Talking Data Podcast » Episodes.

The scale of HDFS continues to soar upward. For large social media and cloud providers, the size of Hadoop clusters is such that it is hard to test out this basic component of classic Hadoop at scale before roll outs. That is another one of those niggling issues that slows Hadoop adoption. At LinkedIn, the challenges of successfully making even small configuration changes across broad arrays of HDFS led a team to create Dynamometer. This load and stress test suite uses actual NameNodes, combined with simulated DataNodes to prove out changes to settings across large the Hadoop data farms that help LinkedIn link folks together. Today, many issues are impossible to test out without running a cluster that is similar in size to what were is used in production, according to Carl Steinbach, senior staff software engineer at LinkedIn. He said that one of the goals of the project is to have positive “upstream effect” on the work of Apache community members’ releases, to, in effect, make testing at scale foremost in the effort. Steinbach’s colleague, engineer Eric Krogen, adds that HDFS developers are looking toward the day when they can find bugs before new versions are committed, rather than six -months later when new software comes to cover very large scale clusters. In this edition of the Talking Data Podcast, the crew speaks with analyst Mike Matchett, of the SmallWorldBigData consultancy, to get a better view into Dynamometer. Testing tools of this kind will only gain in importance going forward, he suggested. “Whatever big data we have today, it’s going to be bigger tomorrow,” said Matchett. Also on tap in this podcast is a discussion of TensorFlow from Google. The company has supported work on this machine learning framework on CPUs, GPUs, and, most recently TPUs – these being Tensor Processing Units build especially to accomplish highly iterative neural network computations. Word is that Google is preparing to open up TPU processing to outsiders that use its Google Cloud Platform. – Jack Vaughan   The post LinkedIn open sources Dynamometer load and stress tester for HDFS appeared first on Talking Data Podcast » Episodes.

The inaugural edition of the Talking Data podcast for 2018 features James Kobielus, analyst, Wikibon, who helps us take the racing pulse of data today. AI, machine learning, deep learning and analytics all come in for consideration. Buckle your seat belt, listen to the podcast and get ready for another tumultuous ride down the big data slope. – Jack Vaughan The post James Kobielus outlines the AI path for big data analytics appeared first on Talking Data Podcast » Episodes.

Machine learning was the big story of 2017, and we here at SearchBusinessAnalytics spent a lot of time talking with businesses who use the technology. In this edition of the Talking Data podcast, we recap some of the best interviews we did on the topic. The interviews look at everything from the role of engineers to avoiding black box functionality in models. Talking with people who actually use a given technology is generally one of the best ways to learn about its real importance, and we spend a lot of time trying to get this perspective from our stories. As we bring 2017 to a close, we hope you can learn something useful from our efforts to drive your machine learning initiatives into the new year. Happy holidays! The post Users discuss their machine learning implementations appeared first on Talking Data Podcast » Episodes.

As 2017 winds down, we invite you to take a look behind the big data curtain. There, you will find data engineers, data scientists, end-users and others working to move a big data concept into production. It doesn’t take much digging to find that more self-service capabilities are needed at each stage in the data life cycle. That is among the take-aways from this latest edition of the Talking Data Podcast. In this and a subsequent episode, Ed Burns and I discuss recent user stories that graced the editorial pages of SearchBusinessAnalytics.com and SearchDataManagement.com – ones that speak to some of the outstanding trends of the year just winding down. One of the telling threads we found was self-service; that is, self-service as it relates to ETL, as it relates to interactive data queries, and as it relates to cluster configuration. In the latter case we have as example restaurateur Panera Bread. The chain is among the company’s with particularly aggressive web initiatives underway. More and more, when lunchtime arrives, incoming orders come in via cell phone. That can stress operational systems. Aware of this threat, Panera Bread built a Spark-Hadoop system to analyze computing needs for the processing involved in handling the lunchtime crush. It was the first in a series of Hadoop apps that Panera is spinning up quickly, after deciding to use automated container configuration software. Panera announced earlier this year that annual digital sales had gone past $1 billion, and that projected digital sales could double by 2019. The ability to let individuals spin up big data jobs at will become handier going forward, one of the company’s engineering leads said. Self-service that empowers more individuals in the data pipeline is a fact of life that IT has generally come to accept. It seems now to be a big part of moving at the speed of innovation. Listen to this podcast and feel free to come back for seconds. The post Big data life cycle gets the self-service treatment appeared first on Talking Data Podcast » Episodes.

Tableau currently has a comfortable relationship with a number of data preparation vendors, most notably Alteryx. But that hasn’t stopped the popular data visualization vendor from developing its own self-service data preparation tool, known as Project Maestro, set to be released before the end of the year. So what does that mean for Tableau’s data prep partnerships? We explore that question in this edition of the Talking Data podcast. We look behind the news to think about how it could ripple throughout the world of analytics software. Will customers still look for standalone data preparation software when they can access good-enough functionality in the higher level software they already own? Will the secondary features of data prep software, like reporting and predictive analytics, be enough to entice customers? For now that all remains up in the air, but we look at the questions in this podcast to see how they may play out. There are still more questions than answers in the still-hot analytics software market, and much remains to shake out. The post Tableau targets data prep with new Project Maestro appeared first on Talking Data Podcast » Episodes.

You push the little valve down, and the music goes round and round. Where does it come out? What does that have to do with PowerBI? Say all you want about deep learning, machine learning and neural networks – eventually enterprises are going to generate reports and dashboards for analytics. In the end, that is where “the music” comes out. For Microsoft that dashboard and reporter often takes the form of PowerBI. This analytics visualizer is an important part of the company’s analytics effort, and one of the earliest examples of its rebirth as a “cloud first” software provider. But, although Power BI Report Server, has its feet in the clouds, it is also striving to play an on-premises role. At the recent Pass Summit 2017 in Seattle, the company enhanced that offerings with scheduled data refresh, Direct Query, and a REST API capabilities. That is part of the discussion on this edition of the Talking Data Podcast. Tune into Talking Data Podcast, where we report from Pass Summit on Power BI, and other Microsoft technology initiatives. The post Reporter at Pass Summit sees PowerBI Report Server – other Microsoft analytics activity appeared first on Talking Data Podcast » Episodes.