Strategies for Enterprise Grade Azure-based Analytics
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Over the past decade, big data implementations have been more sophisticated in particular for organizations operationalizing machine learning, analytics and data engineering. The pressures of data-driven cultures, multiple workload applications such as customer care, fraud management and cross-platform marketing are changing the game. Mixing machine learning with business processes and operationalizing analytics with data engineering practices places burdens on IT teams. Making these advanced data environments all work together is an ongoing challenge. While you can still “swipe and go” to implement data management environments in the cloud for an easy solution, the easy path is often littered with additional costs, higher overhead in terms of maintenance and synchronization. Data savvy organizations are taking a more measured and coordinated approach to their machine learning, analytics and data engineering infrastructures. These proactive approaches speed adoption among business stakeholders and lower administration and governance issues for technologists. Join John L Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Nik Rouda, director of product marketing at Cloudera, to discover how the world of cloud implementations have changed for the better and the future of an enterprise grade cloud environment for your organization using the right resources. Attend this webinar to learn about: Drivers for implementing machine learning, analytics and data engineering with a proactive approach Pitfalls associated with “immediate gratification” implementations How business stakeholders benefit from proactive approaches How proactive implementations improve the workloads of technologists Examples of real world customer implementations 3 things to learn: Drivers for implementing machine learning, analytics and data engineering with a proactive approach Pitfalls associated with “immediate gratification” implementations How business stakeholders benefit from proactive approaches
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Strategies for Enterprise Grade Azure-based Analytics
- 1. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Looking Before You Leap Into the Cloud: Taking a proactive approach to machine learning, analytics, and data engineering in the cloud John L. Myers Managing Research Director EMA Nik Rouda Director of Product Marketing Cloudera
- 2. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Featured Speakers Slide 2 © 2018 Enterprise Management Associates, Inc. John Myers, Managing Research Director, EMA John has nearly 20 years of experience in areas related to business analytics and business intelligence in professional services, sales consulting, product management, industry analysis, and research. He helps organizations solve their analytics problems, whether they related to operational platforms like customer care, billing, or applied analytical applications, such as revenue assurance or fraud management. John established thought leadership in emerging data management paradigms such as big data (combination of multi-structured and relational data sets) applications and NoSQL access data stores. Nik Rouda, Director of Product Marketing, Cloudera Nik is a director of product marketing at Cloudera, covering cloud solutions and core platforms. He has deep enterprise IT infrastructure experience in storage, networking, security, and big data and analytics. He’s worked worldwide in a variety of customer-facing roles at innovative companies such as Riverbed, NetApp, Veritas, and the smart home startup AlertMe.com (acquired by British Gas.) Most recently he was an industry analyst at Enterprise Strategy Group (ESG.)
- 3. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Logistics for Today’s Webinar Slide 3 © 2018 Enterprise Management Associates, Inc. An archived version of the event recording will be available at www.enterprisemanagement.com • Log questions in the chat panel located on the lower left-hand corner of your screen • Questions will be addressed during the Q&A session of the event QUESTIONS EVENT RECORDING A PDF of the speaker slides will be distributed to all attendees PDF SLIDES
- 4. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Join the Conversation To submit questions or comments, use: @JohnLMyers44 @cloudera @nrouda #cloud Slide 4 © 2018 Enterprise Management Associates, Inc.
- 5. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Agenda • Drivers for implementing machine learning, analytics, and data engineering with a proactive approach • Pitfalls associated with “immediate gratification” implementations • How business stakeholders benefit from proactive approaches • How driven implementations improve the workloads of technologists • Examples of real-world customer implementations • Question and Answer Slide 5 © 2018 Enterprise Management Associates, Inc.
- 6. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Topic #1: Drivers for implementing machine learning, analytics, and data engineering with a proactive approach
- 7. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Data-Driven Cultures and Strategies Slide 7 © 2018 Enterprise Management Associates, Inc.
- 8. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Agility and Speed of Delivery: Keys to Supporting the Data-Driven Organization Slide 8 © 2018 Enterprise Management Associates, Inc.
- 9. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Breaking Out of the Walled Garden: Moving Beyond Existing Tools Slide 9 © 2018 Enterprise Management Associates, Inc.
- 10. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Changing the Face of (Big) Data Analytics and Machine Learning Implementations Slide 10 © 2018 Enterprise Management Associates, Inc. .7% of end-user survey respondents have adopted cloud implementation strategies
- 11. 11© Cloudera, Inc. All rights reserved. + • Speed of deployment • Tenant isolation • Self-service • Workload elasticity • Shared storage • Pay-as-you-go • Bring your own tools • Bring your own data • Powerful network CLOUD BENEFITS
- 12. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Future Hybrid- and Multi- Cloud: Across Resources to Manage Costs and Operational Risk Slide 12 © 2018 Enterprise Management Associates, Inc.
- 13. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Topic #2: Pitfalls associated with “immediate gratification” implementations
- 14. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Siloed Data in Individual Cloud Platforms Slide 14 © 2018 Enterprise Management Associates, Inc.
- 15. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Locked Into Vendor Solutions Slide 15 © 2018 Enterprise Management Associates, Inc.
- 16. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Increased Data Movement Increases Complexity Slide 16 © 2018 Enterprise Management Associates, Inc. # The top obstacle to cloud implementation for EMA end-user survey respondents was “increased complexity”
- 17. 17© Cloudera, Inc. All rights reserved. Traditional Applications 17 Data Exploration STORAGE SECURITY GOVERNANCE WORKLOAD MGMT INGEST & REPLICATION DATA CATALOG SQL & BI Analytics STORAGE SECURITY GOVERNANCE WORKLOAD MGMT INGEST & REPLICATION DATA CATALOG Operational Real-Time DB STORAGE SECURITY GOVERNANCE WORKLOAD MGMT INGEST & REPLICATION DATA CATALOG ETL & Data Processing STORAGE SECURITY GOVERNANCE WORKLOAD MGMT INGEST & REPLICATION DATA CATALOG Custom Functions STORAGE SECURITY GOVERNANCE WORKLOAD MGMT INGEST & REPLICATION DATA CATALOG Many data silos, each with its own proprietary tools and infrastructure Different vendors, products, and services on-premises versus in cloud A fragmented approach is difficult, expensive, and risky
- 18. 18© Cloudera, Inc. All rights reserved. – • Proliferation of data copies • Multiple security frameworks • Difficult to troubleshoot workloads • No shared metadata • Unable to track data lineage • Disjointed services • Few on-premises integration services • Proprietary services • Cloud lock-in CLOUD SETBACKS
- 19. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Topic #3: How business stakeholders benefit from proactive approaches
- 20. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Self-Service to Speed Deployments Slide 20 © 2018 Enterprise Management Associates, Inc.
- 21. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Operations to Exploration to Analytics: Integrating Between Workloads Slide 21 © 2018 Enterprise Management Associates, Inc.
- 22. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING More Than Just a Hammer and Nail: Supporting Multiple Tool(sets) for Data Science and Machine Learning Slide 22 © 2018 Enterprise Management Associates, Inc.
- 23. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Building Out Pipelines: Iterative and Effective Data Engineering Slide 23 © 2018 Enterprise Management Associates, Inc. .1% of end-user survey respondents indicated that they can turn data engineering and data prep activities within a single day. Nearly 3 of 10 need a week or longer!
- 24. 24© Cloudera, Inc. All rights reserved. One platform. Multiple workloads. DATA ENGINEERING OPERATIONAL DATABASE ANALYTIC DATABASE DATA SCIENCE DATA PROCESSING • Cost-efficient • Reliable • Scalable • Based on Spark, MapReduce, Hive, and Pig • Supported by workload analytics FAST BI & SQL • Flexibility • Elastic scale • Go beyond SQL • Based on Impala and Hive • SQL dev enviro • Supported by workload analytics MACHINE LEARNING • Fast dev to production • Secure self-serve • Based on Python, R, and Spark • ML dev environment (CDSW) ONLINE & REAL TIME • High throughput, low latency • Strong consistency • Based on Hbase, Kudu, and Spark streaming
- 25. 25© Cloudera, Inc. All rights reserved. Sample Architecture in the Cloud Object Store HBase, Search, Model Server, etc. Kafka + Spark streaming on permanent clusters, for streaming data ingest and processing Spark batch jobs on transient clusters, for processing or machine learning, directly read/write to the object store Impala for exploratory BI on permanent or transient clusters, directly read/write to the object store Serving tier (e.g., HBase, Search) on permanent clusters, serving data to end applications
- 26. 26© Cloudera, Inc. All rights reserved. Cloud Integration to Microsoft Azure Cloudera Azure Data Lake
- 27. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Topic #4: How proactive implementations improve the workloads of technologists
- 28. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Swipe and Go Leads to One-Off Projects Slide 28 © 2018 Enterprise Management Associates, Inc.
- 29. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING More the Merrier: Managing Multiple Environments with Multi-tenancy Slide 29 © 2018 Enterprise Management Associates, Inc.
- 30. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Harmonized Metadata: Increased Security and Coordinated Data Access Slide 30 © 2018 Enterprise Management Associates, Inc. .1% of end-user survey respondents indicated that share metadata sources were important drivers. Over 1 of 5 have the removal of complexity in their strategic vision.
- 31. 31© Cloudera, Inc. All rights reserved. • Shared catalog • Unified security • Consistent governance • Easy workload management • Flexible ingest and replication Open Platform Services Built for multi-function analytics | Optimized for cloud
- 32. 32© Cloudera, Inc. All rights reserved. Multi-cloud Platform as a Service 32© Cloudera, Inc. All rights reserved.
- 33. 33© Cloudera, Inc. All rights reserved. Altus Data Engineering for ETL, machine learning, and data processing • Fast, easy job submission without the cluster management • Built-in workload snalytics for troubleshooting and optimization • Lower costs with transient resources and pay-per-use pricing • Full benefits of isolation + shared data experience
- 34. 34© Cloudera, Inc. All rights reserved. Three immediate use cases for Altus Data Engineering ETL FOR ANALYTIC DB BATCH MACHINE LEARNING ETL OFFLOAD Cloud-native batch preparation for Impala on IaaS or, soon, Altus Analytic DB. Scalable compute for massively-parallel batch machine learning training, scoring, or simulation. Offload batch processing jobs from overburdened on-premises clusters. MLData ScienceETL Analytic DB ETL On-Prem
- 35. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Topic #5: Examples of real-world customer implementations
- 36. 36© Cloudera, Inc. All rights reserved. 36 The modern platform for machine learning and analytics optimized for the cloud DATA CATALOG SECURITY GOVERNANCE WORKLOAD MANAGEMENT INGEST & REPLICATION EXTENSIBLE SERVICES CORE SERVICES DATA ENGINEERING OPERATIONAL DATABASE ANALYTIC DATABASE DATA SCIENCE S 3 ADL S HDF S KUD U STORAGE SERVICES Cloudera Enterprise PRIVATE CLOUDBARE METAL INFRASTRUCTURE DEPLOYMENT OPTIONS SERVICES
- 37. 37© Cloudera, Inc. All rights reserved. DRIVE CUSTOMER INSIGHTS CONNECT PRODUCTS & SERVICES (IoT) PROTECT BUSINESS Connecting qualified candidates to job vacancies with reported 30% reduction in time-to-fill Analyzes equipment data to get a systems view of machine operation Detects fraud and complies with federal regulations and authorities better Cloudera on Azure powering data-driven customers DRIVE CUSTOMER INSIGHTS PROTECT BUSINESS A WORLDWIDE FINANCIAL INSTITUTION
- 38. 38© Cloudera, Inc. All rights reserved. Run anywhere. Deploy any way. Simple Unified Enterprise • Proven at scale • Trusted security • Hybrid or multi-cloud • Platform as a Service • Simplifies operations • Works with your tools
- 39. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING© 2018 Enterprise Management Associates, Inc. • Coordinated data environment • Choice of implementation strategy • Synchronization of assets no matter the cloud provider or implementations Where to go from here?
- 40. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Join the Conversation To submit questions or comments, use: @JohnLMyers44 @cloudera @nrouda #cloud Slide 40 © 2018 Enterprise Management Associates, Inc.
- 41. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Logistics for Today’s Webinar Slide 41 © 2018 Enterprise Management Associates, Inc. An archived version of the event recording will be available at www.enterprisemanagement.com • Log questions in the chat panel located on the lower left-hand corner of your screen • Questions will be addressed during the Q&A session of the event QUESTIONS EVENT RECORDING A PDF of the speaker slides will be distributed to all attendees PDF SLIDES
- 42. IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Question and Answer: Log Questions in the Q&A panel located on the lower left-hand corner Slide 42 © 2018 Enterprise Management Associates, Inc. Learn More About Cloudera at www.cloudera.com Comme RG: Update the late greates JM
Editor's Notes
- 65.5% of implemented next-generation data management implementations like Cloudera CDH are using a form of cloud implementation.
- There are some pros and cons to cloud environments in the context of analytics workloads and data pipelines. The benefits on the left are pretty well-known; cloud service providers have been pushing these for some time now. The disadvantages may be lessons you learn the hard way. We’d like to save you some pain. Cloud is easy to get into for an individual, but very hard to optimize for an enterprise. These are very real problems that are actually exacerbated by the multitude of distinct services available in cloud. In a nutshell, most accidently end up recreating the data silos they had on-premises, and all the extra effort and risk that comes with silos. [ASK: how important is it for you to solve the problems on the right?]
- This is all made tougher to choose because traditional applications use just one kind of data and a single analytic approach. Delivering catalog, security, and governance for that single system is a challenge in bare-metal environments but becomes particularly tough in the cloud, where metadata and policies don’t persist when an elastic workload is dropped. [ASK: do fragmented silos make it hard for you to manage and guarantee security/compliance/etc.? Do you end up often recreating the context, definitions, and permissions of the same data?]
- There are some pros and cons to cloud environments in the context of analytics workloads and data pipelines. The benefits on the left are pretty well-known; cloud service providers have been pushing these for some time now. The disadvantages may be lessons you learn the hard way. We’d like to save you some pain. Cloud is easy to get into for an individual, but very hard to optimize for an enterprise. These are very real problems that are actually exacerbated by the multitude of distinct services available in cloud. In a nutshell, most accidently end up recreating the data silos they had on-premises, and all the extra effort and risk that comes with silos. [ASK: how important is it for you to solve the problems on the right?]
- Top 5 Advanced Analytics objectives Graph analytics (e.g., influencer analysis) Regression algorithms to predict information based on independent variables Decision tree (recursive partitioning) algorithms Feature selection algorithms (e.g., PCA, PLS) Times Series Forecasting and Smoothing
- Linked with change frequencies of daily or weekly. Data engineering departments quickly fall behind in their implementations.
- Cloudera supports four major workloads, and each one addresses different analytics functions. Each stands alone as an industry-leading, open-source approach. Together, they handle your complete data pipeline. We’ve found again and again that the most high-value analytics applications combine these on the same platform with the same data, all managed logically in one place. [ASK: What tools are you using for these today? Are they well integrated from the same vendor? Or do you handle each one separately? At what cost?]
- So now the architecture changes in the cloud. We already talked about why there are separate clusters. Now, let’s talk about how they fit together and how they’re different. Some clusters are going to be persistent, or running 24x7. Others are going to be transient, so spin up for a few hours, run a job, and shut down. Others are going to be clusters with both characteristics, so maybe a persistent cluster that is always up but bursts on occasion and then scales down. They all have different characteristics. Let’s say you have a use case where you are analyzing purchases in real time to help determine when you might be out of stock. The clusters ingesting the data, running Kafka and Spark Streaming, are probably running 24x7 because you would be getting data at all times throughout the day. You probably want HA, DR, and the ability to upgrade the cluster. After the data is ingested, you’re going to need to process it so that your analysts can use it. Spin up a cluster, run an ETL job, and then shut the cluster down. You don’t need HA because if you lose a NN, you can just spin up a new cluster. Security doesn’t matter as much since it’s a single user cluster. Next, the data is probably going to be analyzed. This might be a BI tool and you’re probably going to keep that up 24x7 since people might connect to it at all hours and you want to maintain the metadata. But it’s going to get heavy usage during work hours, so you probably want to spin up additional nodes to support all those users. Finally, maybe you have an application that is using a NoSQL backend to keep track and notify folks responsible for supply chain that they need to restock items. Again, that’s going to be a persistent cluster since that’s an application that will always be running.
- Fundamentally, Cloudera leverages Azure Virtual Machines (from D, G, and L series) to provision nodes in a customer’s Azure environment to provide elastic scale. Azure Storage (Premium and Standard) is also used to independently scale out cluster storage capacity on demand. Azure ExpressRoute is used to accommodate customers who need a fast, private network from an on-premises or colocation facility to transfer data to Cloudera in Azure. Power BI integration provides visual analytics capability for end users. Cloudera has also recently released the integration to Azure Data Lake Store (ADLS) to enable greater performance and scalability, leveraging the cloud object store technology built for big data in Azure. Cloudera is also available in the Azure Marketplace (since 2015) to enable fast, one-click deployment of Cloudera Enterprise Data Hub to Azure customers. What used to take weeks or more on-premises can now be accomplished in under an hour.
- Underlying everything is our SDX, which has the shared metadata catalog that facilitates consistent data management and operations everywhere and anywhere. SDX also includes comprehensive, granular security to protect against threats and unified governance for the audit and search capabilities that the modern world demands, especially with standards like PCI-DSS and GDPR. For IT, that means you can set policies once and enforce them everyone. For analysts, data scientists, and others, SDX enables self-service and increases productivity. For the business, it means understanding customers better, connecting products and services, and protecting the business with confidence.
- Cloudera Altus is our platform as a service offering, offering ETL, machine learning, and data processing on Amazon Web Services and Microsoft Azure. In the not too distant future, you’ll see us move beyond data engineering to analytic and data science workloads, delivered via any underlying cloud platform, including Amazon, Microsoft, and Google.
- The first Altus experience we’re delivering is data engineering as a service. Think about ETL for machine learning and analytics. Altus is available on AWS today, and we are planning to release on Azure in the future. Altus runs on cloud-native infrastructure, so it’s easy to spin up transient clusters that have large-scale compute, process the data, and write your output back to a cloud object store like Amazon S3. Altus supports our standard CDH distribution, which includes Hive, Spark, and Hive on Spark. You can see the Altus portal here to the right of the text on the screen. You can access Altus with a simple login, and then work within the portal or through a CLI if you want to submit jobs programmatically. Jobs are considered first-class objects on Altus. You can submit, clone, troubleshoot, and sort by jobs. Many of you are running upward of 100 workloads in a day. You may want to view a history of those jobs, so you can find and troubleshoot failed jobs and run them again. Because Altus is a PaaS, you don’t need to deal with installing software, worrying about cluster configuration, resource management, or patching.
- The usual issue to data movement. They need to have figured out a story for that. Otherwise, it becomes a painful conversation. What are some of the patterns that we have seen people use successfully? If they already backup data to S3, that works.
- Here we see it all together: 4+ analytics workloads, 4 deployment models, and 1 shared data experience. Again, no one else offers this choice and common controls all together.
- ADECCO Adecco uses Cloudera Enterprise on Azure to power its Search and Match solution, connecting qualified candidates to job vacancies with reported 30% reduction in time-to-fill and a 20% reduction in job board spend in its first 90 days. JOY GLOBAL Cloudera on Azure makes it easy for Joy Global teams in the field to analyze equipment data form their own and third-party PLC-based equipment to get a systems view of machine operation. WORLDWIDE FINANCIAL INSTITUTION (BLINDED) Detects fraud (money laundering) and complies with federal regulations and authorities better ---- DETAIL/SPECIFICS ---- Adecco: Search Technologies Helps Adecco Group Significantly Improve Recruiter Efficiency http://www.prweb.com/releases/2015/11/prweb13100660.htm (PRWeb: Search Technologies Press Release (12/2/2015). Add’l excerpts: “Search and Match Application Based on Cloudera and Solr Improves Recruiter Response Times and Fill Rates” “Adecco was recently short-listed for the prestigious Cloudera Business Impact Award at Hadoop World 2015” Joy Global: Joy Global is a world leader in making heavy-duty mining equipment for both surface and underground excavation. The company had a legacy IoT predictive maintenance system built in 2008 and had challenges meeting scale and performance demands from its business. As they grew and monitored more and more equipment and an increasing user base, they started to feel pressure points on the architecture that made it difficult for them to scale and support the global user base. Joy Global has a wide variety of data types that are collected from mining machines: machine pressure, temperature, currents, voltages, and a range of other sensor data, all of which are sampled at high frequencies and are increasing at an exponential rate. A single machine could have 800 data points generating about 30-50,000 unique time-stamped records in a one-minute file. Cloudera on Azure makes it easy for Joy Global teams in the field to analyze data that they pull in from Joy Global equipment (such as longwall systems, shovels, wheel loaders, continuous miners, and others), and also from third party PLC-based equipment to get a systems view of machine operation. This expanded capability allowed one of Joy Global’s longwall mining operator customers to acquire data not just from the Joy longwall system, but also from ancillary equipment. Using Impala on HDFS in Azure and an Hbase store for time-series data, the team is also able to provide access to this data through self-service visualization reports. The ability to create custom reports and ad-hoc analysis from a common set of data enabled regional engineers to answer customers' questions faster. An example of an outcome from this engagement was production optimization and the doubling of weekly cutting hours from their Joy Global longwall system. Joy Global has realized some significant cost savings on their cloud infrastructure by moving to Azure. They are able to deliver all of the data for Joy Global customers with much less compute than they had in the previous system, with a lot more data and intelligence. As reputation for quality demands a 24x7 monitoring operation, Joy Global relies on Cloudera and Microsoft Azure to maintain that quality. Worldwide Financial Institution: Worldwide Financial Institution needed visibility and access to data in order to better understand what is happening with their products and business at all levels within the organization. In addition to providing insightful information to Executives, it will allow the business insight to critical information in order to make revisions for the way we do business today. The current data mart sits within the PCI zone, making access and self-service challenging. Information needs to be accessible and accurate, which requires a framework that needs to be integrated, repeatable, and scalable to add to future reporting needs. The new solution allowed the Institution to detect fraud (money laundering) to comply with federal authorities.
- We allow you to run anywhere and deploy any way that you choose, giving you a simple, unified enterprise experience. We simplify your operations so you can work with familiar tools, and focus on your job without having to worry about cloud infrastructure management. “Unified” means that you can have a similar experience across any workload, whether in a hybrid or multi-cloud environment, and whether in a PaaS or infrastructure as a service deployment. Lastly, everything we do at Cloudera is built to be enterprise-grade, proven at great scale with a trusted security model, and have consistent governance and workload management.