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70-776 exam Dumps Source : Performing broad Data Engineering with Microsoft Cloud Services
Test Code : 70-776
Test title : Performing broad Data Engineering with Microsoft Cloud Services
Vendor title : Microsoft
: 69 existent Questions
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Pepperdata gives Pre- and post-Migration Workload evaluation, utility efficiency evaluation and SLA Validation for Cloudwick AWS Migration purchasers
SAN FRANCISCO, March 27, 2019 /PRNewswire/ -- Strata statistics conference - sales space 926 -- Pepperdata, the leader in massive statistics application performance administration (APM), and Cloudwick, leading issuer of digital trade services and solutions to the global 1000, nowadays announced a collaborative offering for enterprises migrating their great facts to Amazon web capabilities (AWS). Pepperdata gives Cloudwick with a baseline of on-premises performance, maps workloads to most dependable static and on-demand situations, diagnoses any concerns that arise perquisite through migration, and assesses performance after the stream to exist positive the identical or stronger performance and SLAs.
"The biggest challenge for organizations migrating broad statistics to the cloud is ensuring SLAs are maintained while not having to commit materials to utterly re-engineer functions," observed Ash Munshi, Pepperdata CEO. "Cloudwick and Pepperdata develop positive workloads are migrated correctly via analyzing and setting up a metrics-primarily based efficiency baseline."
"Migrating to the cloud devoid of looking at the performance information first is risky for groups and if a migration is not executed right, the complaints from traces of trade are unavoidable," mentioned consequence Schreiber, time-honored manager for Cloudwick. "without Pepperdata's metrics and analysis earlier than and after the migration, there is no approach to prove efficiency levels are maintained within the cloud."
For Cloudwick's AWS Migration functions, Pepperdata is build in on purchasers' existing, on-premises clusters — it takes below half-hour — and immediately collects over 350 true-time operational metrics from functions and infrastructure resources, including CPU, RAM, disk I/O, and network usage metrics on each job, task, user, host, workflow, and queue. These metrics are used to research efficiency and SLAs, precisely map workloads to applicable AWS situations, and supply cost projections. as soon as the AWS migration is complete, the identical operational metrics from the cloud are collected and analyzed to examine efficiency results and validate migration success.
To exist trained more, cease by route of the Pepperdata sales space (926) at Strata statistics conference March 25-28 at Moscone West in San Francisco.
About PepperdataPepperdata (https://pepperdata.com) is the chief in broad information application performance administration (APM) options and capabilities, solving utility and infrastructure considerations during the stack for builders and operations managers. The trade partners with its valued clientele to supply proven products, operational event, and profound handicap to deliver predictable efficiency, empowered users, managed fees and managed boom for their massive statistics investments, each on-premise and in the cloud. leading companies dote Comcast, Philips Wellcentive and NBC time-honored depend upon Pepperdata to deliver massive data success. based in 2012 and headquartered in Cupertino, California, Pepperdata has attracted govt and engineering skill from Yahoo, Google, Microsoft and Netflix. Pepperdata traders include Citi Ventures, Costanoa Ventures, Signia challenge partners, Silicon Valley statistics Capital and Wing challenge Capital, along with main high-profile particular person investors. For extra counsel, discuss with www.pepperdata.com.
SAN FRANCISCO, March 26, 2019 /PRNewswire-PRWeb/ -- Clairvoyant and Cazena, these days announced a partnership and original solution for agencies migrating massive records and analytics to the cloud. The Cloud Migration Accelerator will quicken up tasks and aid exist positive a success results as agencies build in coerce hybrid facts architectures or circulation current workloads to cloud suppliers AWS and Microsoft Azure.
The partnership marks an ingenious collaboration between data and determination engineering enterprise Clairvoyant, and Cazena, the chief in great facts as a carrier. The corporations made the joint announcement nowadays in San Francisco, where they are collectively for the Cloudera partner Kickoff and Strata facts conference happening this week.
"Clairvoyant has a protracted, a success history with offering information platforms and analytics solutions to corporations. Cazena's technology is confirmed to support companies migrate and modernize within the age of the cloud. Clairvoyant's handicap in records engineering, computer studying and analytics, and utility support models will exist paired with Cazena's expertise to carry robust options for their purchasers in an accelerated fashion," stated Shekhar Vemuri, Co-Founder and CTO, Clairvoyant.
the brand original Cloud Migration Accelerator includes features from Clairvoyant, and leverages Cazena's totally-Managed SaaS for Cloudera. The influence is the fastest, most appropriate and low risk respond for businesses emigrate to Cloudera on AWS or Microsoft Azure, in addition to on-premise. This offers organisations with a solution that's entirely-managed and delivered in four weeks or much less. With Cazena and Clairvoyant, organisations can in the reduction of natural challenge prices, improve time to market by using 50%+, and execute not should hire any further team of workers for deployment or operations. Former Hortonworks purchasers can additionally quicken up their path to the original unified Cloudera records Platform.
The Cloud Migration Accelerator comprises:
"we are excited to team up with Clairvoyant to present a seamless migration undergo for organizations. Cloud migrations are complicated for tremendous groups, requiring original talents, expertise and up to date architectures. together, Cazena and Clairvoyant tackle these challenges, empowering firms to pressure sooner company results and maximize their investments in huge statistics and analytics in the cloud," celebrated Prat Moghe, Founder & CEO, Cazena.
study greater in regards to the Cloud Migration Accelerator here or contact both trade for extra assistance.
About ClairvoyantClairvoyant is a learning and preference engineering company that helps groups navigate, recognise, and maximize the cost of huge facts. The enterprise provides managed services, massive facts and analytics consulting, for the Hadoop ecosystem to one of the world's largest manufacturers. Clairvoyant customers depend on its profound upright learning and optimum-in-class capabilities to build products and structures using records, analytics and the cloud. based in 2012, Clairvoyant is headquartered in Phoenix with offices in the U.S, India, and Canada. For extra tips, discuss with https://www.clairvoyantsoft.com.
About CazenaCazena is the handiest absolutely-Managed broad data as a carrier, with deepest SaaS solutions for companies which are deployed on-demand, radically gash back costs and require zero admin time. firms around the globe depend on Cazena for creation-competent information lakes, analytics, information science, desktop studying, statistics engineering, information warehousing and hybrid cloud expansions. solutions combine Cloudera, Microsoft Azure, AWS and other ultimate-of-breed software – together with cloud automation, storage, analytics engines, enterprise protection, 24x7 monitoring, hybrid capabilities, built-in DevOps and extra. Cazena was centered by the trade veterans behind the time-honored Netezza information equipment. exist trained more at https://www.cazena.com.
pretty much each step of the IC utensil roam generates some amount of information. however certain steps generate a mind-boggling volume of statistics, not each and every of which is of equal value. The problem is determining what’s essential for which materials of the design flow. That determines what to extract and loop back to engineers, and when that needs to exist achieved with a purpose to enrich the reliability of increasingly complicated chips and in the reduction of the ordinary time to tapeout.
This has develop into basic in safety-vital markets, where requirements require tracking of data evaluation. but past that, examining data can support pinpoint concerns in design and manufacturing, including concerns stemming from thermal and other actual outcomes to detecting fugitive anomalies and latent defects. along with that records, original ideas for information analysis are being developed, including using digital twins, artificial intelligence and computing device gaining learning of.
In considerable judgment verification flows, the data from testbench code insurance is analyzed exhaustively to present which constituents of the RTL version of designs had been exercised, and to what extent they hold got been demonstrated. This facts evaluation is essential to the common sense verification stream. different areas of the IC design roam generate a lot of statistics that historically has no longer been analyzed as exhaustively because it simply offers an illustration of even if chips are functioning as anticipated. that's altering, however.
“With the increasing emergence of digitalization, self enough and AI in just about every wee thing, each and every facts is starting to exist in value, talked about Joe Sawicki, executive vice chairman of IC EDA at Mentor, a Siemens company. “The transforming into variety of protection specifications too require original ranges of monitoring tactics and mandate documentation of the statistics generated by route of each and every these procedures and equipment. increasingly these requisites claim extensive checking out in any respect levels of development—from IP to obstruct to chip to PCB to eu to all electronic device to terminate product electrical/mechanical—and that check facts has to exist documented.”
It additionally must exist checked out in context, which is the Place one of the original tools rotate into vital. In car and a few medical gadgets, for example, one rig may requisite to fail over to another. here's the Place AI and ideas corresponding to digital twins slot in, because these contraptions requisite to exist proven in virtual situations running perquisite application before the items are manufactured and verified in the existent world.
“All that has to exist demonstrated, and the methods of checking out and consequences should exist documented,” Sawicki referred to, noting that to achieve this, integrated options are elementary to not only enable design and verification groups to check and collect facts on the plane of the methods, throughout engineering disciplines, and as a digital twin, however then to develop exercise of that statistics to supply extra tough subsequent-technology products and much more productive ways to manufacture these items. “Having extra statistics, and sharing that records between tools will permit the rising-generation of AI/ML-powered tools to exist better educated to duty projects sooner and with improved accuracy—not just for the tools in typical IC EDA stream, however for each and every of the rig involved in pile improved, more comprehensive digital twins.”
massive facts complications and approachesComplicating each and every of here is the growing to exist complexity and measurement of chips, and the amount of records being generated by every wee thing from layout to regressions. That statistics has to exist study and analyzed, nevertheless it too requires an actionable plan.
is the sheer magnitude of facts sizes, remarked, so the order of the day is first, examine the information; 2nd, anatomize it; third, create analytics or actionable choices. “What’s the cost with each and every this data and the reviews,” requested Vic Kulkarni, vice chairman and common manager of the Semiconductor trade Unit at ANSYS. “That’s a typical question. as an example, it’s very benchmark to hold between 21 billion and 25 billion transistors for AI chips, GPUs and CPUs. in case you study any original startups doing the AI chips for ADAS, for crypto, for any resolution-making, one of the convolutional neural networks are are each and every in that latitude. These usually hold a hundred billion-plus related regions/nodes, with each of these having diverse connections. because of this, 1 trillion sheer RC components are on some of these ADAS sort chips.”
riding this evaluation from the technical facet is the multi-physics of vigor and signal integrity, reliability, in addition to electromagnetic consequences. “A multi-physics strategy is required over time correct from the considerable historical analog transistor days, to micron, submicron, nanometers, and now to 7, 5, 4, 3nm,” Kulkarni observed. “Moore’s legislations has persevered, youngsters at 3nm it’s questionable, however each and every of these outcomes hold started to develop into so vital that further and further physics gets brought. Multi-physics outcomes dawn including up as expertise shrinks.”
Ansys has introduced computer gaining learning of on correct of facts management to establish mistaken violations in its electromigration and circuit reliability applied sciences, which examine what patterns will trigger reliability or getting older concerns. “opting for what facts to control and the route to anatomize it is intricate,” he referred to. this is above each and every actual for automotive purposes, the Place LIDAR, radar and cameras each and every produce streaming facts. effectively managing and inspecting this data may additionally require various kinds of processors used in mixture, including GPUs, CPUs and FPGAs, he mentioned.
Combining distinct applied sciences is fitting greater middling for analyzing everything from hardware and application performance to how they adhere to safeguard necessities.
“We started with the identification of parallelism, however found that’s no longer definitely the concern for original forms of purposes,” stated Max Odendahl, CEO of Silexica. “There could exist enough concurrency and adequate parallelism. The query is more at the gadget degree—how to combine the dynamic records with what already has been written, and how that suits perquisite into a mixed model. Engineering groups don’t requisite extra parallelism. They should figure out what’s happening in the equipment. What happens if it doesn’t work? execute they requisite extra parallelism? execute they requisite improved scheduling? Is a wider bus essential? Does the device want mapping of the threads to selected cores since it’s too dynamic? What’s the foundation explanation for bugs? They haven't any clue. each and every they execute is entangle a broad volume of facts. they can survey at exceed charts of a thousand lines, but it surely doesn’t in reality inform them anything.”
What is required is a greater focused route to combine the information that has been captured in accordance with software monitoring or dynamic evaluation, and then relate this returned to the rational application architecture to combine them perquisite into a unified model, Odendahl observed. “What am i able to execute with this? i will exist able to assess that the route I implemented is in reality the route it’s unique. Or if somebody begun hacking away, and administration says it’s a product, now I each and every of sudden deserve to execute each and every types of validation exams, taking pictures each and every of that information to pomp it in fact makes sense. That’s what we're hearing on an everyday foundation. once I even hold this connection, i will accumulate lots of insights and check out to locate the root cause. Or it may well exist there are two runs with the equal algorithms, but they behave in a different way. If each and every I’m is an exceed chart, that’s in fact challenging to discover. but when I even hold source code connection, i will inform that in Line 12 there become a recursive algorithm one time. It went 10 iterations, but in keeping with the original facts, I did 200 iterations, and that’s why they feel it appears distinctive. i can simplest execute that if I connect these two worlds collectively.”
What’s differentIn the common verification space, the scope of verification at each and every times has been seen as a holistic system. but the definition of holistic has changed as chips more and more are more tightly integrated into systems, and as systems engage with different programs.
“For the design and verification group, it’s basically about predictability within the schedule, making an attempt to exist mindful if they are being as productive and effective as they probably will too be, and even if they are focused on an acceptable degree of best for the start of their product,” celebrated Larry Melling, product management director within the gadget & Verification neighborhood of Cadence. “Verification is a stack, and the stack carries the execution of the verification methods and various kinds of engines. The subsequent layer, which includes layers of abstraction, contains even if i can execute this faster if i exploit a more abstract illustration of anything, and it’s equivalent ample for these cases. It’s variety of balancing the engines with abstraction to claim i can accumulate more efficiency by route of providing some plane of abstraction. Then on the precise level, even if they talk a all lot about coverage and every wee thing else, for those who seek recommendation from clients it’s each and every about bugs.”
during this space, verification administration has advanced from coverage-driven to metric-pushed, which is the mainstream these days. this may evolve into facts-driven verification, which requires a special route of looking at the verification difficulty.
“It’s learning to inquire of the appropriate questions of the data after which visualizing those records sets in a routine that says, ‘I remark some thing perquisite here,’” Melling observed. “If they can compile that information, and accumulate this tips, and execute this analysis, then they can truly inform it what the subsequent step is, as antagonistic to having an engineer in the core of the loop the entire time. It’s really an iterative procedure, but the decent news is with these things it might roam perquisite away when you accumulate the engine built. So it’s doing the facts assortment with disbursed facts methods, such that you should question distributed facts and create wonderful records sets in a supple method. As that occurs, they may soundless start to remark more speedy progress in terms of analytics and closed-loop remarks forms of methods.”
Others agree. “within the verification house, analytics statistics and metrics are used both to develop more suitable progress on a particular undertaking and to enhance rig for future initiatives,” mentioned Dominik Strasser, vp of engineering at OneSpin solutions. “definitely, analytics and metrics are on the very coronary heart of simultaneous useful verification. coverage metrics and verification effects are the keys to figuring out what has been verified, highlighting gaps in verification, and deciding on when the system is finished. Metrics and consequences from formal evaluation can exist integrated with these from simulation and emulation with the tips of original industrial equipment. outcomes can too exist returned-annotated to the customary verification plan, and insurance results can exist used to check a minimal set of simulation checks. These steps converge on full verification and purposeful sign-off extra rapidly.”
right through formal verification runs, extra metrics will too exist gathered on which formal engines are most constructive for various kinds of designs and assertions, Strasser mentioned. “modern formal rig hold many such engines. consultants may additionally wish to manage them without leisurely however most clients simply want the very best results (proofs and bugs) as quickly as possible.”
And now, with computing device researching, the algorithms that opt for which engine to sprint accumulate greater and more desirable in line with what is found to work most useful. This, in turn, accelerates the verification technique on a given design. but the precise win is that over time the collective learning from dissimilar tasks improves the formal rig themselves, Strasser noted.
Closing the loopWhile lots of data is captured each and every the route through the design and verification method, what’s more vital is the technique of shooting records from the existent world and feeding that back into the verification process.
here is each and every about making a preference on how application is definitely working on this hardware—with the perquisite insight, talked about Rupert Baines, CEO of UltraSoC. “There are software equipment, reminiscent of sampling profilers, however they are wrong. they're intrusive. They simplest remark some activity as a result of they're sampling. They interpolate their own dependencies (heisinbugs), and crucially they hold an consequence on performance—rather significantly. In contrast, hardware-based mostly analytics are true-time, exhaustive coverage. This strategy doesn't affect conduct and doesn't affect performance. They too supply perception into things utility cannot see—interconnects, reminiscence controllers and caches. The latter two are famously complicated and infamously answerable for a lot of efficiency concerns. And as they exist alert of, caches can too exist accountable for lots of protection issues too, so analytics that may realize them can too exist valuable.”
knowing the interaction between application and hardware is critical. “software efficiency is contingent upon hardware,” Baines stressed. “software integration and verification is now one the largest sole expenses in SoC development, and it's getting worse with multi-core designs. noticeably commonly, subtle aspects of hardware design can hold major affects on utility efficiency and conduct. Integration and verification to realize those, optimize performance, notice security concerns, and many others., is very tough. there's a necessity for analytics that gives real-time, non-intrusive, system-degree insights.”
targeting solutionsStill, statistics by itself is barely statistics. “For statistics to become advantageous it needs to exist contextualized,” pointed out Stelios Diamantidis, product marketing director of the design community at Synopsys. “should you start thinking concerning the issue and contextualizing the facts, it actually each and every the time starts with what problem are you trying to remedy. And that’s where we're. We’re starting to admiration a wee more advantageous, and a bit more systematically, how challenges and information will too exist combined to develop facts more significant. If they just seem on the raw statistics, it's overwhelming in the context of design and verification where lots of the issues we’re attempting to decipher complications are NP-comprehensive issues and therefore are very problematic in terms of containing and addressing them.”
Even the Place records is attainable, the usefulness of that statistics varies enormously. “the provision of the correct records—not simply the quantity of facts—is vital to boost predictability, reliability and enhance design and verification outcomes,” talked about Tim Whitfield, Arm’s vp of strategy. “Arm has applied a standardized framework (schema) to assemble the perquisite records throughout each and every tasks and engineering flows. This enables for a considerable deal wider and accelerated software of information science strategies because of more convenient transfer of results across tasks and close availability of appropriate historic records for working towards and tuning. They remark advantages of leveraging facts in power, efficiency, insurance, reduced testing efforts, failure evaluation, verification, and so on.” Whitfield referred to that Arm has created committed groups to bootstrap laptop discovering. “one of the key hub of attention areas is embedded intelligence and on-line laptop learning flows, by which adaptive algorithms study from original information 24/7 and immediately replace engineering flows to enrich effects. facts assortment is standardized throughout corporations and projects.”
ConclusionA natural purpose amongst each and every chipmakers and EDA organizations is to seize a wide array of facts per job and per circulate, which can exist fed returned to multiply the effectivity and effectiveness of engineering flows. however is too more and more crucial to suppose about data when it comes to many different points than just the rig it sits in. This goes smartly beyond just the introduction and collection of records. It too extends to coverage, security and privacy.
“We raise supervision of loads of very, very colossal groups, and that they depart beyond probably the most essential plane of supervision about their IP,” mentioned David Hsu, product advertising and marketing director of the verification group at Synopsys. “So within the undergo of having a technique or a coordinated view of how they cope with this, it’s at each and every times going to exist evolving. however it can’t exist viewed as, ‘Some materials of the company are going to execute this, some rig are going to execute that, every other organizations are going to try this.’ That’s a no go. the manner they requisite to examine here's in fact much less in regards to the information and extra about what must exist answered despite the fact that shoppers are available in and say, ‘There’s each and every this AI stuff that’s really cool. What are you guys doing there?’ They suppose that’s in reality not the appropriate question to ask. The appropriate question to inquire of is, ‘I’ve obtained this horrendous challenge in X, Y or Z, and there doesn’t seem to exist any algorithmic route to transparent up it because it’s beyond an NP-complete issue. What can you do? here's going to execute me both now or next 12 months, delight help.’ these are the sort of areas that are pertinent to what we’re talking about now.”
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Pepperdata Provides Pre- and Post-Migration Workload Analysis, Application Performance Assessment and SLA Validation for Cloudwick AWS Migration Customers
SAN FRANCISCO, March 27, 2019 /PRNewswire/ -- Strata Data Conference - Booth 926 -- Pepperdata, the leader in broad data Application Performance Management (APM), and Cloudwick, leading provider of digital trade services and solutions to the Global 1000, today announced a collaborative offering for enterprises migrating their broad data to Amazon Web Services (AWS). Pepperdata provides Cloudwick with a baseline of on-premises performance, maps workloads to optimal static and on-demand instances, diagnoses any issues that arise during migration, and assesses performance after the roam to ensure the identical or better performance and SLAs.
"The biggest challenge for enterprises migrating broad data to the cloud is ensuring SLAs are maintained without having to devote resources to entirely re-engineer applications," said Ash Munshi, Pepperdata CEO. "Cloudwick and Pepperdata ensure workloads are migrated successfully by analyzing and establishing a metrics-based performance baseline."
"Migrating to the cloud without looking at the performance data first is risky for organizations and if a migration is not done right, the complaints from lines of trade are unavoidable," said consequence Schreiber, generic Manager for Cloudwick. "Without Pepperdata's metrics and analysis before and after the migration, there is no route to prove performance levels are maintained in the cloud."
For Cloudwick's AWS Migration Services, Pepperdata is installed on customers' existing, on-premises clusters — it takes under 30 minutes — and automatically collects over 350 real-time operational metrics from applications and infrastructure resources, including CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue. These metrics are used to anatomize performance and SLAs, accurately map workloads to appropriate AWS instances, and provide cost projections. Once the AWS migration is complete, the identical operational metrics from the cloud are collected and analyzed to assess performance results and validate migration success.
To learn more, cease by the Pepperdata booth (926) at Strata Data Conference March 25-28 at Moscone West in San Francisco.
About PepperdataPepperdata (https://pepperdata.com) is the leader in broad data Application Performance Management (APM) solutions and services, solving application and infrastructure issues throughout the stack for developers and operations managers. The company partners with its customers to provide proven products, operational experience, and profound expertise to deliver predictable performance, empowered users, managed costs and managed growth for their broad data investments, both on-premise and in the cloud. Leading companies dote Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver broad data success. Founded in 2012 and headquartered in Cupertino, California, Pepperdata has attracted executive and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata investors include Citi Ventures, Costanoa Ventures, Signia Venture Partners, Silicon Valley Data Capital and Wing Venture Capital, along with leading high-profile individual investors. For more information, visit www.pepperdata.com.
SAN FRANCISCO, March 26, 2019 /PRNewswire-PRWeb/ -- Clairvoyant and Cazena, today announced a partnership and original solution for enterprises migrating broad data and analytics to the cloud. The Cloud Migration Accelerator will quicken up projects and abet ensure successful outcomes as enterprises implement hybrid data architectures or roam existing workloads to cloud providers AWS and Microsoft Azure.
The partnership marks an innovative collaboration between data and determination engineering company Clairvoyant, and Cazena, the leader in broad Data as a Service. The companies made the joint announcement today in San Francisco, where they are together for the Cloudera partner Kickoff and Strata Data Conference happening this week.
"Clairvoyant has a long, successful history with delivering data platforms and analytics solutions to enterprises. Cazena's technology is proven to abet enterprises migrate and modernize in the age of the cloud. Clairvoyant's expertise in data engineering, machine learning and analytics, and application support models will exist paired with Cazena's technology to deliver robust solutions for their customers in an accelerated fashion," said Shekhar Vemuri, Co-Founder and CTO, Clairvoyant.
The original Cloud Migration Accelerator includes services from Clairvoyant, and leverages Cazena's Fully-Managed SaaS for Cloudera. The result is the fastest, most efficient and low risk solution for enterprises to migrate to Cloudera on AWS or Microsoft Azure, as well as on-premise. This provides enterprises with a solution that's fully-managed and delivered in four weeks or less. With Cazena and Clairvoyant, enterprises can reduce overall project costs, improve time to market by 50%+, and execute not requisite to hire any additional staff for deployment or operations. Former Hortonworks customers can too accelerate their path to the original unified Cloudera Data Platform.
The Cloud Migration Accelerator includes:
"We are excited to team up with Clairvoyant to tender a seamless migration undergo for enterprises. Cloud migrations are complicated for great companies, requiring original skills, expertise and modern architectures. Together, Cazena and Clairvoyant address these challenges, empowering enterprises to drive faster trade results and maximize their investments in broad data and analytics in the cloud," said Prat Moghe, Founder & CEO, Cazena.
Learn more about the Cloud Migration Accelerator here or contact either company for more information.
About ClairvoyantClairvoyant is a data and determination engineering company that helps organizations navigate, realize, and maximize the value of broad data. The company provides managed services, broad data and analytics consulting, for the Hadoop ecosystem to some of the world's largest brands. Clairvoyant clients reliance on its profound upright learning and best-in-class services to build products and platforms using data, analytics and the cloud. Founded in 2012, Clairvoyant is headquartered in Phoenix with offices in the U.S, India, and Canada. For more information, visit https://www.clairvoyantsoft.com.
About CazenaCazena is the Only Fully-Managed broad Data as a Service, with private SaaS solutions for enterprises that are deployed on-demand, radically reduce costs and require zero admin time. Enterprises around the globe reliance on Cazena for production-ready data lakes, analytics, data science, machine learning, data engineering, data warehousing and hybrid cloud expansions. Solutions combine Cloudera, Microsoft Azure, AWS and other best-of-breed software – along with cloud automation, storage, analytics engines, enterprise security, 24x7 monitoring, hybrid capabilities, built-in DevOps and more. Cazena was founded by the industry veterans behind the Popular Netezza data appliance. Learn more at https://www.cazena.com.
SAN MATEO, Calif., March 26, 2019 (GLOBE NEWSWIRE) -- Arcadia Data, provider of the first visual analytics software indigenous to the cloud and Apache™ Hadoop®, today announced the expanded support of its modern trade intelligence (BI) capabilities for the leading cloud-based data warehouses, as well as a visionary blueprint for cloud-native analytics and BI. Built from the ground up to work in distributed and decoupled architectures, Arcadia Data enables the next generation of scalability, elasticity, performance, lower administrative overhead, and usage-based pricing of cloud environments.
Fundamental to Arcadia Enterprise is the decoupling of BI compute and storage and its indigenous ability to directly anatomize data in cloud expostulate storage dote Amazon S3 and Azure Data Lake Storage Gen2 (ADLS) with no data movement or duplication. With the compute tier decoupled, customers can independently grow and shrink the amount of resources dedicated to BI workloads to optimize spending in a usage-based model. This enables a cloud-native BI service that provides direct access to data in expostulate stores and a truly elastic and scale-out architecture.
Arcadia Enterprise is certified to sprint on each and every the leading Apache Hadoop distributions, including Amazon EMR which enables large-scale analytics as an Amazon-managed service. It is integrated with shared metadata services dote Amazon Glue, and with leading cloud database and data warehouse services including Amazon Athena, Redshift, RDS, Aurora, Confluent Cloud, Databricks, Google BigQuery, and Snowflake which will let customers incorporate modern architectures into their production analytic and BI applications.
Arcadia Data was built from inception as the first distributed BI platform for large-scale analytics directly within modern data platforms with no data duplication or movement, helping create the category for “in-data-lake BI platforms.” Now, Arcadia Data gives this powerful handicap to customers with the flexibility to exercise the leading cloud storage services with a fully-featured BI platform for each and every data sets, including data warehouses and cloud expostulate stores. This comprehensive, service-oriented data architecture enables different workloads and multi-function services in the cloud to support both batch analytics and real-time streaming in a single, user-friendly interface. The most recently released version of its flagship product, Arcadia Enterprise 5.0, now lets organizations leverage modern BI capabilities dote natural language query (NLQ), high-scale visual mapping, and AI-driven dashboard acceleration in their digital transformation initiatives.
For individuals involved to accumulate started with Arcadia Data on cloud databases, the company’s free-to-download and unlimited-use desktop BI tool, Arcadia Instant, provides connectivity to Amazon Athena, Confluent Cloud, Databricks, Google BigQuery, and Snowflake, in addition to the existing set of supported data sources. This lets data analysis professionals immediately start exploring and visualizing data in their cloud data stores. Once valuable insights are discovered with Arcadia Instant, there is an smooth pathway to upgrade to the fully-secure and scalable Arcadia Enterprise with Smart Acceleration™ for production-ready, tall performance analytic and BI applications. Arcadia Enterprise provides integration connectivity to additional cloud data stores dote Amazon Redshift, RDS, Aurora, S3, and ADLS.
"Neustar must manage and regularly deliver, from petabytes of data, timely insights to customers," said Satya Ramachandran, vice president, engineering, Neustar. "Arcadia Data works seamlessly with the cloud-based pieces of their data infrastructure, allowing us to support thousands of customer terminate users without sacrificing performance. Making Arcadia Data a cornerstone of their data strategy enables us to generate granular insights on great volumes of data, empowering better decisions and improved customer experiences for their clients."
Unlike traditional BI architectures, cloud-native BI provides the scale and trade agility critical for organizations to gain a competitive edge in today’s data-driven world. According to Forrester, “In advanced insights-driven companies, 38% of data and analytics determination makers mutter they hold already fully replaced their on-premises enterprise BI with cloud-based deployments; 40% mutter the identical about replacing departmental or line-of-business BI platforms with cloud.” (Move BI to the Cloud to Achieve Greater Agility, Forrester Research, inc., by Boris Evelson, December 4, 2018)
“With data management increasingly stirring to expostulate storage and cloud data warehouses, organizations naturally await that BI applications can too capitalize from the scale of data and real-time analytics," said Priyank Patel, co-founder and chief product officer of Arcadia Data. "Yet, traditional BI architectures cannot meet those expectations because they are a mismatch for cloud-native environments. For instance, traditional BI can only sprint on subsets of data, and though data ingestion can happen in existent time, analytics lags while waiting on ETL batch cycles. Arcadia Data provides a cloud-native approach that frees companies from relying on those outdated analytic patterns, delivering an agile architecture that meets the original plane of analytics scalability and performance companies demand."
ResourcesBI users can download Arcadia Instant to start their modern BI journey in the cloud today.
To learn more about Arcadia Enterprise 5.0 and what it means to hold an in-data-lake, cloud-native BI platform, register for this upcoming webinar.
About Arcadia DataArcadia Data provides the first AI-driven in-data-lake BI and analytics platform that delivers the scale, performance, and agility trade users requisite to ascertain and productionize real-time insights. Its flagship product, Arcadia Enterprise, was built from inception to sprint natively in the cloud and broad data platforms to streamline the self-service analytics process on data in Amazon S3, Microsoft ADLS, Apache Hadoop®, Apache Spark®, and Apache Kafka®.. It enables real-time, high-definition insights in exercise cases dote data lakes, cybersecurity, connected IoT devices, and customer intelligence. Arcadia Enterprise is deployed by some of the world’s leading brands, including Procter & Gamble, Nokia, Royal Bank of Canada, Kaiser Permanente, HPE, and Neustar. To learn more, supervene @ArcadiaData or visit www.arcadiadata.com.
Press Contact:Sammy TotahEmail: Arcadiadata@bocacommunications.com
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