IBM® dashDB™ is a fast, fully managed, cloud data warehouse that utilizes integrated analytics to rapidly deliver answers. dashDB’s unique in-database analytics, R predictive modeling and business intelligence tools free you to analyze your data and get precise insights, quicker. dashDB is simple to get up and running with rapid provisioning in IBM Bluemix™. You can test the solution or start using dashDB for no charge, for up to one gigabyte of data and then just $50 US
per month for 20 gigabytes of data storage. Larger instance sizes with multi-terabyte capacity are available as you grow your data, and as your users require a dedicated environment. Massively Parallel Processing (MPP) enables even faster query speeds as well as larger scale data sets.
dashDB Enterprise MPP is a new fully managed cloud data warehouse service with massive scale and performance. Powered by IBM's network cluster architecture, dashDB MPP is an easy to use, self service solution for building: standalone data warehouses; data science data marts; hybrid warehousing; development and QA environments; and analytics for NoSQL. It is available through IBM Bluemix along with IBM's other Cloud Data Services, including Cloudant and SQL DB.
Many Oracle pros are looking to take their data warehousing strategy to the cloud, but have been waiting for a cloud solution that offers both compatibility and ease of use. Well, the wait is over - with IBM dashDB, you can leverage your existing Oracle (as well as SQL) application skills, and get all the cost, scalability and performance advantages of a fully managed data warehousing service in the IBM Cloud.
Cloud and Software as a Service (SaaS) can make a huge impact on a business. Unfortunately, most start the evaluation of SaaS from an IT perspective and traditional data center advantages (i.e. on-premises costs, staffing and savings). While savings are important, cloud is about agility and speed. For these reasons, line-of-business (LOB) leaders have been more interested in SaaS solutions. Learn how Cognos Business Intelligence on Cloud and IBM dashdb make it simple to get started with collaboration, reporting and analytics.
John Park, Offering Manager, for IBM Cloud Data Services covers the touchstones for tomorrow’s information systems: data and integration. Stovepipe applications are no longer acceptable, and siloed data sources must evolve and open up to the full enterprise. All this in an environment where more is expected faster, and at a lower cost. If your GIS doesn’t watch out, it will be replaced by less capable alternatives that “fit better” into mainstream IT. But dashDB, a cloud-native offspring of DB2, can provide a bridge that keeps both sides happy. This session introduce this popular cloud data warehousing solution and illustrate how it works in concert with ArcGIS. You will learn about the built-in geospatial functions in dashDB and how you can easily use them to build applications rapidly. You’ll see an application that uses weather data and mobile application data to calculate insurance risk, detect potential fraud, and prevent damage.
Snowflake + Syncsort: Get Value from Your Mainframe DataPrecisely
Your business wants to solve problems for your customers, not spend time managing silos of disconnected data that comes from on-premises solutions and new cloud applications. More and more organizations are looking to solve this problem by investing in cloud-based storage and analytics platforms such as Snowflake. However, data from systems such as mainframes can be a challenge to bring into cloud data warehouses. Together, Snowflake and Syncsort offer you the ability to get the full picture of your data – whether its mainframe or from a cloud application. View this webinar on how Snowflake and Syncsort are working together to get you back to what is essential for your business.
View this webcast on-demand to learn:
• Best practices for extracting your mainframe data
• Advantages of using Snowflake for your cloud data warehouse needs
• Common challenges faced by businesses trying to access mainframe data for use in cloud data warehouses
• How Syncsort is helping organizations gain strategic value from their mainframe data
dashDB Enterprise MPP is a new fully managed cloud data warehouse service with massive scale and performance. Powered by IBM's network cluster architecture, dashDB MPP is an easy to use, self service solution for building: standalone data warehouses; data science data marts; hybrid warehousing; development and QA environments; and analytics for NoSQL. It is available through IBM Bluemix along with IBM's other Cloud Data Services, including Cloudant and SQL DB.
Many Oracle pros are looking to take their data warehousing strategy to the cloud, but have been waiting for a cloud solution that offers both compatibility and ease of use. Well, the wait is over - with IBM dashDB, you can leverage your existing Oracle (as well as SQL) application skills, and get all the cost, scalability and performance advantages of a fully managed data warehousing service in the IBM Cloud.
Cloud and Software as a Service (SaaS) can make a huge impact on a business. Unfortunately, most start the evaluation of SaaS from an IT perspective and traditional data center advantages (i.e. on-premises costs, staffing and savings). While savings are important, cloud is about agility and speed. For these reasons, line-of-business (LOB) leaders have been more interested in SaaS solutions. Learn how Cognos Business Intelligence on Cloud and IBM dashdb make it simple to get started with collaboration, reporting and analytics.
John Park, Offering Manager, for IBM Cloud Data Services covers the touchstones for tomorrow’s information systems: data and integration. Stovepipe applications are no longer acceptable, and siloed data sources must evolve and open up to the full enterprise. All this in an environment where more is expected faster, and at a lower cost. If your GIS doesn’t watch out, it will be replaced by less capable alternatives that “fit better” into mainstream IT. But dashDB, a cloud-native offspring of DB2, can provide a bridge that keeps both sides happy. This session introduce this popular cloud data warehousing solution and illustrate how it works in concert with ArcGIS. You will learn about the built-in geospatial functions in dashDB and how you can easily use them to build applications rapidly. You’ll see an application that uses weather data and mobile application data to calculate insurance risk, detect potential fraud, and prevent damage.
Snowflake + Syncsort: Get Value from Your Mainframe DataPrecisely
Your business wants to solve problems for your customers, not spend time managing silos of disconnected data that comes from on-premises solutions and new cloud applications. More and more organizations are looking to solve this problem by investing in cloud-based storage and analytics platforms such as Snowflake. However, data from systems such as mainframes can be a challenge to bring into cloud data warehouses. Together, Snowflake and Syncsort offer you the ability to get the full picture of your data – whether its mainframe or from a cloud application. View this webinar on how Snowflake and Syncsort are working together to get you back to what is essential for your business.
View this webcast on-demand to learn:
• Best practices for extracting your mainframe data
• Advantages of using Snowflake for your cloud data warehouse needs
• Common challenges faced by businesses trying to access mainframe data for use in cloud data warehouses
• How Syncsort is helping organizations gain strategic value from their mainframe data
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
Solving enterprise challenges through scale out storage & big compute finalAvere Systems
Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management.
The slides were from a webinar presented January 24, 2017. The audience learned:
- How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand
- Best practices for efficient use of cloud compute and storage resources
- Overcoming the need for file systems within a hybrid cloud environment
- Understand how to eliminate latency between cloud and data center architectures
- Learn how to best manage simulation, analytics, and big data workloads in dynamic environments
- Look at market dynamics drawing companies to new storage models over the next several years
Presenters communicated a foundation to build infrastructure to support ongoing demand growth.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
View the webinar here - https://bit.ly/2ErkxYY
Enterprises are moving their data warehouse to the cloud to take advantage of reduced operational and administrative overheads, improved business agility, and unmatched simplicity.
The Impetus Workload Transformation Solution makes the journey to the cloud easier by automating the DW migration to cloud-native data warehouse platforms like Snowflake. The solution enables enterprises to automate conversion of source DDL, DML scripts, business logic, and procedural constructs. Enterprises can preserve their existing investments, eliminate error-prone, slow, and expensive manual practices, mitigate any risk, and accelerate time-to-market with the solution.
Join our upcoming webinar where Impetus experts will detail:
Cloud migration strategy
Critical considerations for moving to the cloud
Nuances of migration journey to Snowflake
Demo – Automated workload transformation to Snowflake.
To view - visit https://bit.ly/2ErkxYY
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
Learn how Power BI and Snowflake can work together to bring a best-in-class data and analytics experience to your enterprise. You can combine Snowflake’s easy to use, robust, and scalable data platform with Power BI’s data visualization, built-in AI, and collaboration platform to create a data-driven culture for everyone.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
From the Data Work Out event:
Performant and scalable Data Science with Dataiku DSS and Snowflake
Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data.
A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.
Delivering rapid-fire Analytics with Snowflake and TableauHarald Erb
Until recently, advancements in data warehousing and analytics were largely incremental. Small innovations in database design would herald a new data warehouse every
2-3 years, which would quickly become overwhelmed with rapidly increasing data volumes. Knowledge workers struggled to access those databases with development intensive BI tools designed for reporting, rather than exploration and sharing. Both databases and BI tools were strained in locally hosted environments that were inflexible to growth or change.
Snowflake and Tableau represent a fundamentally different approach. Snowflake’s multi-cluster shared data architecture was designed for the cloud and to handle logarithmically larger data volumes at blazing speed. Tableau was made to foster an interactive approach to analytics, freeing knowledge workers to use the speed of Snowflake to their greatest advantage.
Part of proper governance in Power BI means taking proper care of what goes on in your tenant. Here's a list of areas you need to watch for and some helpful telemetry to start collecting.
Introducing Direct Database Access with Snowflake + IntrinioIntrinio
Intrinio is proud to announce our new integration with Snowflake. In keeping with our mission to make data simpler and more useful, we’re now offering direct database access to our business customers. Watch the full webinar here: https://www.youtube.com/watch?v=zobU34StN2I&t=6s
Webinar: Don't believe the hype, you don't need dedicated storage for VDI NetApp
This webinar covers how the combination of SolidFire and Citrix XenDesktop enables customers to confidently support the storage demands of a virtual desktop environment in a multi-tenant or multi-application environment.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Details:
• DevOps and Business Intelligence?
• CI/CD Pipelines: What are they?
• Database Deployments: State based vs Migration based
• Snowflake features for CI/CD
• Azure DevOps: Build and Release Pipelines
• Putting it all together: End to End solution
• Demo
Put your Data Warehouse in Cloud which is faster, cheaper and better; then focus only on Business development and everything else is taken by the Cloud Service Provider.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back – and the roadblock that stops many promising machine learning projects. After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Syncsort software helps the data engineer every step of the way.
Building on the process of finding and matching duplicates to resolve entities, the next step is to set up a continuous streaming flow of data from data sources so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow – into the arms of machine learning models.
Some of your sources may already be streaming, but the rest are sitting in transactional databases that change hundreds or thousands of times a day. The challenge is that you can’t affect performance of data sources that run key applications, so putting something like database triggers in place is not the best idea. Using Apache Kafka or similar technologies as the backbone to moving data around doesn’t solve the problem of needing to grab changes from the source pushing them into Kafka and consuming the data from Kafka to be processed. If something unexpected happens – like connectivity is lost on either the source or the target side, you don’t want to have to fix it or start over because the data is out of sync.
View this 15-minute webcast on-demand to learn how to tackle these challenges in large scale production implementations.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
Solving enterprise challenges through scale out storage & big compute finalAvere Systems
Google Cloud Platform, Avere Systems, and Cycle Computing experts will share best practices for advancing solutions to big challenges faced by enterprises with growing compute and storage needs. In this “best practices” webinar, you’ll hear how these companies are working to improve results that drive businesses forward through scalability, performance, and ease of management.
The slides were from a webinar presented January 24, 2017. The audience learned:
- How enterprises are using Google Cloud Platform to gain compute and storage capacity on-demand
- Best practices for efficient use of cloud compute and storage resources
- Overcoming the need for file systems within a hybrid cloud environment
- Understand how to eliminate latency between cloud and data center architectures
- Learn how to best manage simulation, analytics, and big data workloads in dynamic environments
- Look at market dynamics drawing companies to new storage models over the next several years
Presenters communicated a foundation to build infrastructure to support ongoing demand growth.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
View the webinar here - https://bit.ly/2ErkxYY
Enterprises are moving their data warehouse to the cloud to take advantage of reduced operational and administrative overheads, improved business agility, and unmatched simplicity.
The Impetus Workload Transformation Solution makes the journey to the cloud easier by automating the DW migration to cloud-native data warehouse platforms like Snowflake. The solution enables enterprises to automate conversion of source DDL, DML scripts, business logic, and procedural constructs. Enterprises can preserve their existing investments, eliminate error-prone, slow, and expensive manual practices, mitigate any risk, and accelerate time-to-market with the solution.
Join our upcoming webinar where Impetus experts will detail:
Cloud migration strategy
Critical considerations for moving to the cloud
Nuances of migration journey to Snowflake
Demo – Automated workload transformation to Snowflake.
To view - visit https://bit.ly/2ErkxYY
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
Learn how Power BI and Snowflake can work together to bring a best-in-class data and analytics experience to your enterprise. You can combine Snowflake’s easy to use, robust, and scalable data platform with Power BI’s data visualization, built-in AI, and collaboration platform to create a data-driven culture for everyone.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
From the Data Work Out event:
Performant and scalable Data Science with Dataiku DSS and Snowflake
Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data.
A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.
Delivering rapid-fire Analytics with Snowflake and TableauHarald Erb
Until recently, advancements in data warehousing and analytics were largely incremental. Small innovations in database design would herald a new data warehouse every
2-3 years, which would quickly become overwhelmed with rapidly increasing data volumes. Knowledge workers struggled to access those databases with development intensive BI tools designed for reporting, rather than exploration and sharing. Both databases and BI tools were strained in locally hosted environments that were inflexible to growth or change.
Snowflake and Tableau represent a fundamentally different approach. Snowflake’s multi-cluster shared data architecture was designed for the cloud and to handle logarithmically larger data volumes at blazing speed. Tableau was made to foster an interactive approach to analytics, freeing knowledge workers to use the speed of Snowflake to their greatest advantage.
Part of proper governance in Power BI means taking proper care of what goes on in your tenant. Here's a list of areas you need to watch for and some helpful telemetry to start collecting.
Introducing Direct Database Access with Snowflake + IntrinioIntrinio
Intrinio is proud to announce our new integration with Snowflake. In keeping with our mission to make data simpler and more useful, we’re now offering direct database access to our business customers. Watch the full webinar here: https://www.youtube.com/watch?v=zobU34StN2I&t=6s
Webinar: Don't believe the hype, you don't need dedicated storage for VDI NetApp
This webinar covers how the combination of SolidFire and Citrix XenDesktop enables customers to confidently support the storage demands of a virtual desktop environment in a multi-tenant or multi-application environment.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Details:
• DevOps and Business Intelligence?
• CI/CD Pipelines: What are they?
• Database Deployments: State based vs Migration based
• Snowflake features for CI/CD
• Azure DevOps: Build and Release Pipelines
• Putting it all together: End to End solution
• Demo
Put your Data Warehouse in Cloud which is faster, cheaper and better; then focus only on Business development and everything else is taken by the Cloud Service Provider.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back – and the roadblock that stops many promising machine learning projects. After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Syncsort software helps the data engineer every step of the way.
Building on the process of finding and matching duplicates to resolve entities, the next step is to set up a continuous streaming flow of data from data sources so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow – into the arms of machine learning models.
Some of your sources may already be streaming, but the rest are sitting in transactional databases that change hundreds or thousands of times a day. The challenge is that you can’t affect performance of data sources that run key applications, so putting something like database triggers in place is not the best idea. Using Apache Kafka or similar technologies as the backbone to moving data around doesn’t solve the problem of needing to grab changes from the source pushing them into Kafka and consuming the data from Kafka to be processed. If something unexpected happens – like connectivity is lost on either the source or the target side, you don’t want to have to fix it or start over because the data is out of sync.
View this 15-minute webcast on-demand to learn how to tackle these challenges in large scale production implementations.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Daum evaluated solutions that could address the limitations in the resource-intensive analysis required by Hadoop and the NoSQL database management systems. To meet the data analysis requirements for its search engine and the Internet services businesses, the company selected Pivotal Greenplum Database, which connects to Hadoop and enables the co-processing of both structured and unstructured data within a single solution.
To learn more, visit pivotal.io/big-data/pivotal-greenplum-database.
Which Change Data Capture Strategy is Right for You?Precisely
Change Data Capture or CDC is the practice of moving the changes made in an important transactional system to other systems, so that data is kept current and consistent across the enterprise. CDC keeps reporting and analytic systems working on the latest, most accurate data.
Many different CDC strategies exist. Each strategy has advantages and disadvantages. Some put an undue burden on the source database. They can cause queries or applications to become slow or even fail. Some bog down network bandwidth, or have big delays between change and replication.
Each business process has different requirements, as well. For some business needs, a replication delay of more than a second is too long. For others, a delay of less than 24 hours is excellent.
Which CDC strategy will match your business needs? How do you choose?
View this webcast on-demand to learn:
• Advantages and disadvantages of different CDC methods
• The replication latency your project requires
• How to keep data current in Big Data technologies like Hadoop
Using real time big data analytics for competitive advantageAmazon Web Services
Many organisations find it challenging to successfully perform real-time data analytics using their own on premise IT infrastructure. Building a system that can adapt and scale rapidly to handle dramatic increases in transaction loads can potentially be quite a costly and time consuming exercise.
Most of the time, infrastructure is under-utilised and it’s near impossible for organisations to forecast the amount of computing power they will need in the future to serve their customers and suppliers.
To overcome these challenges, organisations can instead utilise the cloud to support their real-time data analytics activities. Scalable, agile and secure, cloud-based infrastructure enables organisations to quickly spin up infrastructure to support their data analytics projects exactly when it is needed. Importantly, they can ‘switch off’ infrastructure when it is not.
BluePi Consulting and Amazon Web Services (AWS) are giving you the opportunity to discover how organisations are using real time data analytics to gain new insights from their information to improve the customer experience and drive competitive advantage.
Modern apps and services are leveraging data to change the way we engage with users in a more personalized way. Skyla Loomis talks big data, analytics, NoSQL, SQL and how IBM Cloud is open for data.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to info@insideanalysis.com, or tweet with #DBSurvival.
SendGrid Improves Email Delivery with Hybrid Data WarehousingAmazon Web Services
When you received your Uber ‘Tuesday Evening Ride Receipt’ or Spotify’s ‘This Week’s New Music’ email, did you think about how they got there?
SendGrid’s reliable email platform delivers each month over 20 Billion transactional and marketing emails on behalf of many of your favorite brands, including Uber, Airbnb, Spotify, Foursquare and NextDoor.
SendGrid was looking to evolve its data warehouse architecture in order to improve decision making and optimize customer experience. They needed a scalable and reliable architecture that would allow them to move nimbly and efficiently with a relatively small IT organization, while supporting the needs of both business and technical users at SendGrid.
SendGrid’s Director of Enterprise Data Operations will be joining architects from Amazon Web Services (AWS) and Informatica to discuss SendGrid’s journey to a hybrid cloud architecture and how a hybrid data warehousing solution is optimized to support SendGrid’s analytics initiative. Speakers will also review common technologies and use cases being deployed in hybrid cloud today, common data management challenges in hybrid cloud and best practices for addressing these challenges.
Join us to learn:
• How to evolve to a hybrid data warehouse with Amazon Redshift for scalability, agility and cost efficiency with minimal IT resources
• Hybrid cloud data management use cases
• Best practices for addressing hybrid cloud data management challenges
The data that your business collects is constantly growing, making it increasingly difficult for traditional systems to keep up with resource demands. Understanding your big data can help you serve your customers better, improve product quality, and grow your revenue, but you need a platform that can handle the strain.
In hands-on tests in our datacenter, the Scalable Modular Server DX2000 from NEC processed big data quickly and scaled nearly linearly as we added server nodes. In our k-means data cluster analysis test, a DX2000 solution running Apache Spark and Red Hat Enterprise Linux OpenStack Platform processed 100GB in approximately 2 minutes. We also saw that as we doubled the number of server nodes, the DX2000 solution cut analysis time in half when processing the same amount of data, producing excellent scalability.
The Scalable Modular Server DX2000 by NEC is a good choice when you’re ready to put big data to work for you.
Cisco Big Data Warehouse Expansion Featuring MapR DistributionAppfluent Technology
Learn more about the Cisco Big Data Warehouse Expansion Solution featuring MapR Distribution including Apache Hadoop.
The BDWE solution begins with the collection of data usage statistics by Appfluent. Then the BDWE solution optimizes Cisco UCS hardware for running the MapR Distribution including Hadoop, software for federating multiple data sources, and a comprehensive services methodology for assessing, migrating, virtualizing, and operating a logically expanded warehouse.
Priming your digital immune system: Cybersecurity in the cognitive eraLuke Farrell
Learn how cognitive security may be a powerful tool in addressing challenges security professionals face.
New capabilities for a
challenging era
Security leaders are working to address three gaps
in their current capabilities
—
in intelligence, speed
and accuracy. Some organizations are beginning to
explore the potential of cognitive security solutions
to address these gaps and get ahead of their risks
and threats. There are high expectations for this
technology. Fifty-seven percent of the security
leaders we surveyed believe that it can significantly
slow the ef forts of cybercriminals. The 22 percent of
respondents who we call “Primed” have started their
journey into the cognitive era of cybersecurity
—
they
believe they have the familiarity, the maturity and the
resources they need. To begin the journey, it is
important to explore your weaknesses, determine
how you want to augment your capabilities with
cognitive solutions and think about building education
and investment plans for your stakeholders.
Cybersecurity in the cognitive era: Priming your digital immune systemLuke Farrell
Learn how cognitive security may be a powerful tool in addressing challenges security professionals face
Organisations are exploring cognitive security solutions to address their gaps
How companies are managing growth, gaining insights
and cutting costs in the era of big data.
Top reasons to change your database:
1. Lower total cost of ownership
2. A platform for rapid reporting
and analytics
3. Increased scalability and
availability
4. Support for new and emerging
applications
5. Flexibility for hybrid environments
6. Greater simplicity
This datasheet highlights new and enhanced features in IBM SPSS Statistics 24. Extend the value of time-series forecasting using temporal causal modeling, new analytical techniques including geospatial analytics and enhancements to programmability, performance and accessibility.
Manchester Police and ProActive Crime PreventionLuke Farrell
How can predictive policing drive proactive crime prevention?
Manchester Police Department Protects and serves the 110,000 citizens of Manchester, New Hampshire. They needed a smarter way to decide where its 237 officers should patrol. They worked with IBM to help predict where crimes were likely to occur... to find out more please contact me directly
What does data tell you about the customer journey?Luke Farrell
Taken from the IBM Analytics slideshare...
In this omnichannel world, consumers leave clues about their purchasing decisions at every touch point. What data analytics can you leverage to optimize your marketing message and merchandising? Well, it turns out, a lot.
Your Cognitive Future in the Retail IndustryLuke Farrell
Taking from Tero Angeria IBM
Cognitive + retail = the future
Welcome to the age of cognitive computing, where intelligent machines simulate human brain capabilities to help solve society’s most vexing problems. For retail,cognitive computing has already arrived, and its potential to transform the industry is enormous. Cognitive systems are driving more personalized
shopping experiences and helping unearth customer trends. Our research reveals that retail leaders globally are poised to embrace this groundbreaking technology more holistically and, by doing so, will redefine the future in retail.
Dear Grey Area
Why to you have to make everything so complicated?
Sincerely
Fan of Black and White
What is the view from IBM on the wonderfully vague notion of "Big Data"
1- Lower total cost of ownership
2- A platform for rapid reporting and analytics
3- Increased scalability and availability
4- Support for new and emerging applications
5- Flexibility for hybrid environment
6- Greater simplicity
(Original share from Francisco González Jiménez)
IBM Sales Performance Management (SPM) for DummiesLuke Farrell
IBM Incentive Compensation Management enables organizations to automate the process of administering, calculating, reporting and analyzing variable-based pay programs. It enhances incentive compensation management by increasing accuracy, reducing costs and improving visibility into sales performance and compensation plans. IBM Incentive Compensation Management is offered on-premise or as a cloud solution to help you get up and running more quickly, and reduce the burden on IT teams.
IBM Incentive Compensation Management provides:
An intuitive, easy-to-use interface with wizards and drag-and-drop features to increase your organization's agility and sales force alignment.
Workflow management and audit tracking to support incentive compensation management (ICM) processes such as communication, inquiries/disputes, splits, adjustments and more.
Reports, dashboards, analytics and modeling for more efficient ICM.
Automated processes, scheduling and task management to streamline system activities and reduce administration costs.
High performance and scalability across multiple plans, participants and transactions (from hundreds to billions).
Collaboration, mobility and unified analytics when accessed through IBM Concert
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. Technical White Paper
IBM Analytics
IBM dashDB
Cloud-based data warehousing as-a-service,
built for analytics
IBM®
dashDB™
is a fast, fully managed, cloud data warehouse that
utilizes integrated analytics to rapidly deliver answers. dashDB’s unique
in-database analytics, R predictive modeling and business intelligence
tools free you to analyze your data and get precise insights, quicker.
dashDB is simple to get up and running with rapid provisioning
in IBM Bluemix™
. You can test the solution or start using dashDB
for no charge, for up to one gigabyte of data and then just $50 US
per month for 20 gigabytes of data storage. Larger instance sizes with
multi-terabyte capacity are available as you grow your data, and as
your users require a dedicated environment. Massively Parallel
Processing (MPP) enables even faster query speeds as well as larger
scale data sets.
IBM dashDB provides the simplicity of a data warehouse appliance
as a service, with the agility and scalability of the cloud for any size
organization. You can rapidly compose analytic applications using the
rich set of developer and complementary services in IBM Bluemix or
with your favorite on-premises tools.
Data warehousing before dashDB
Historically, building a data warehouse was a painstaking endeavor.
You had to decide on specific data warehousing software and then
determine and secure the proper balance of hardware and storage to
allocate for it. Once you decided on the physical makeup of the data
warehouse, you would then be tasked on both building the physical
system as well as the logical data models that would support your
initiative. When you needed to expand the data warehouse (and you
definitely would, as the data that you are collecting is always growing
and new applications are built on top), you would then need to
purchase new allocations of processing power, storage and software.
Highlights
• Gain instant access to critical
business insights
• Load, analyze and visualize your data
at rapid speeds
• Upload data from multiple sources
and integrate with R
• Achieve greater insights with in-database
predictive analytic algorithms
• Extend on-premises data warehouse
environments to the cloud
• Analyze JSON data through native
integration with IBM Cloudant
2. Technical White paper
IBM Analytics
2
The entire process introduced risks each and every time
you would alter the data warehouse. Did you keep the
proper balance of processing to data? Did you procure the
proper hardware? Was the data allocated across the system
correctly? Was the hardware out of date and incompatible
with the newest technology on the market? Was the software
in need of updates? At any point in the growth process, you
were opening yourself up to a host of issues that could occur.
Add to this the fact that you had to pay for capacity, even
if you were not yet using it. You and your organization were
taking all of the risk.
Enter the data warehouse appliance
Data warehouse appliances helped alleviate much of the
pain of building your own data warehouse. These systems
came pre-configured and integrated the data warehouse for
analytical performance. All you really had to do was pick
out your model and size, and once plugged in and turned
on, you loaded your data into the warehouse.
The allocations of hardware and software were already
done for you, yet the appliance still required you to buy
a new system when you hit your resource limits. With the
amount of growing data, new applications and new users,
implementing a new warehouse could get expensive as you
dynamically scale. Add to the situation upgrades, patches,
maintenance and overall hardware depreciation, and you
are still left with much of the maintenance of a traditional
data warehouse.
The data warehouse appliance is still an essential part of an
organization’s decision management system, but is there
some sort of complementary technology that would alleviate
some of these growing pains? Enter IBM dashDB.
Benefits of the cloud
Through the scalable and on demand nature of cloud
computing, you can get the data warehouse up and running
very quickly with rapid cloud provisioning. Since there isn’t
any infrastructure investment, you are empowered with true
business agility. You buy what you want, when you want.
You are in charge.
As a fully managed cloud service, the day-to-day backend
of maintaining dashDB is taken care of for you. This
maintenance is not only limited to whatever fixpacks may
or may not be applied (these actually become irrelevant
because you never know that they have occurred until
you read the corresponding documentation) but also the
versioning. With dashDB, our dedicated engineers and
developers are continuously building new functionality,
compatibility and integrations into the product — and for
you, this happens automatically.
Protect your data with dashDB
From design to deployment, dashDB is optimized to provide
thorough security coverage of your data. dashDB includes
multiple layers of security such as automatic encryption for
data at rest and in transit, database activity monitoring with
IBM InfoSphere®
Guardium®
, advanced database access
control, and deployment hardening.
Security starts with design and development best practices.
dashDB is developed using practices such as risk assessment,
threat modeling and static and dynamic code analysis using
IBM AppScan®
.
Grow faster by focusing on your business,
not the business of data warehousing
The simplicity of cloud is key — dashDB is a fully managed
service and you have just heard a few of the cloud’s inherent
benefits, such as automatic fixpacks and versions, rapid cloud
provisioning and business agility. By utilizing dashDB as a
service, you pay for the product as you go, based on what you
use. This plan differs greatly from buying a complete data
warehouse. With dashDB, as your data grows, you pay for
added capacity — simple and controlled.
In concert with simple pricing, the fact that this solution
is a service and not a hardware cluster or appliance helps
you to grow as your business demands. There is no purchase,
installation and testing of new software and hardware — you
simply grow as you need in the cloud.
3. Technical White paper
IBM Analytics
3
Built-in performance with in-memory
technology delivers fast answers
What makes dashDB different from other cloud-based data
warehouses? A lot. At the core of dashDB is IBM’s BLU
Acceleration technology.
IBM BLU Acceleration is an in-memory database technology
that provides cutting-edge warehousing performance without
the typical constraints of in-memory solutions. Since dashDB
is built on BLU, it maintains all of its advantages:
• Advanced processing: dashDB does not require the
entire dataset to fit in-memory while still processing
at lightning-fast speeds — it uses a series of patented
algorithms that nimbly handle in-memory data processing.
• Prefetching of data: dashDB is designed to anticipate and
“prefetch” data just before it’s needed, and to automatically
adapt to keep necessary data in or close to the CPU.
• No decompression required: dashDB preserves
the order of data and performs a broad range of
operations — including joins and predicate evaluations —
on compressed data, without the need for decompression,
drastically speeding the processing of data.
• Data skipping: When dealing with big data, there is a
good chance that you don’t need everything in the data
warehouse to answer a particular query. dashDB’s BLU
Acceleration is designed to automatically determine which
data would not qualify for analysis within a particular
query. Large quantities of irrelevant data can then be
skipped over during a query, saving you time and resources.
MPP capabilities enable faster queries
and massive data sets
dashDB’s MPP builds upon the benefits of the standard
dashDB service with even more speed and scale, so you can
handle much larger data sets. The MPP architecture is a
networked cluster of servers working in parallel to speed up
query fulfillment. In the dashDB MPP cluster, multiple
servers work on the same query simultaneously, and the
processing of a query at each server is further parallelized
across all the processors.
In a standard architecture, parallelization occurs only at the
processor level. With an MPP architecture, a query is broken
up into pieces so that multiple servers, each with their own
local storage and compute capacity, are working on separate
pieces of the data. This team effort drastically speeds up the
querying process, and reduces I/O requirements. Each
individual server working on a query in MPP leverages BLU
dynamic in-memory columnar store technology, which
minimizes I/O even further and achieves an order of
magnitude in speed when compared to conventional row-
store databases.
With MPP, performance improvements are increased with
each new server added to the network cluster. For example, if
a query takes one hour in a standard architecture using a
single server, it would take approximately 15 minutes with an
MPP cluster utilizing just four servers. Adding one more
server, for a total of five, reduces the query time to 12
minutes; six servers reduces the query time further to 10
minutes; and so on. Therefore, with dashDB MPP, scaling out
is as simple as adding additional servers to your cluster.
Built for analytics to help you
understand your data and business
The evolution of in-memory processing is moving faster
than ever due to the substantial growth in data volumes that
organizations are tackling. As hardware and memory are
commoditized, we are able to push more of the data to the
memory and process it there.
Organizations used to have to wait to receive analytic
reports from the data warehouse; yet with new advances
with in-memory computing, this is no longer the case.
Results are now available for decision making in real-time.
This enhanced speed also makes further analysis of the
results feasible for the analyst who may wish to dig deeper
into results.
The data warehouse is now being used as the main data
store for analytics. Therefore, it makes sense to bring analytics
to the data warehouse rather than move data out
to analyze it elsewhere.
4. Technical White paper
IBM Analytics
4
In-database analytics for greater
efficiency and performance
Building upon the performance of BLU Acceleration,
dashDB also integrates IBM Netezza®
Analytics for fully
integrated in-database advanced analytics. The same
technology has been used by IBM Netezza appliances
and IBM PureData™
for Analytics systems.
What this means is that with dashDB, you get a myriad
of predictive modeling algorithms built directly into the
database. These algorithms are available whenever you
want to use them.
An example of the algorithms included with dashDB are:
• Linear regression
• Decision tree clustering
• K-means clustering
• Esri-compatible geospatial extensions
By running the analytics natively in the database, where the
data resides, your organization can gain huge efficiencies.
Rather than having to extract the data, send it somewhere
else, stage it and then process it, you are leaving the data
where it resides (in the data warehouse) and then applying
the analytics directly to it.
Compatibility with advanced tooling
like R and IBM Watson Analytics
R is an open source programming language that was
developed for advanced data analysis and graphical
visualization. It can be used to analyze data from many
different data sources including external files or databases.
dashDB integrates R for predictive modeling through an
R runtime alongside the data. A web console can be used
to load data and perform analytics within minutes. Data
analysis could include SQL, BI tools, or R scripts and
models. With dashDB and open source R, your analytical
options are broad and varied.
IBM dashDB includes RStudio — a fully integrated R
development environment designed to provide quick,
R-based predictive analytics. RStudio provides an R language
code completion feature, integrated help for R packages,
file management capabilities and much more. If you need
to install additional R packages, you can do so easily from
within RStudio.
dashDB was developed with the larger business intelligence
ecosystem in mind. The dashDB service works natively with
core IBM technologies like IBM Watson™
Analytics, IBM
Cognos®
BI, IBM DataWorks and others, yet it definitely
does not stop there. dashDB was built to work with IBM’s
myriad of business partners and BI tool sets including Looker,
Aginity Workbench, Tableau and many others.
The IBM partner ecosystem is growing rapidly, and you can
connect multiple third-party tools to the dashDB service.
For example:
• Connect IBM InfoSphere Data Architect to design
and deploy your database schema
• Connect Esri ArcGIS to perform geospatial analytics
and map publishing with your data
• Connect an IBM Cognos server to run Cognos reports
against your data
• Connect SQL-based tools such as Tableau, Microstrategy,
or Microsoft Excel to manipulate or analyze your data
• Connect your Bluemix™
applications that require an
analytics database
• Connect Aginity Workbench to migrate Netezza data
models and data to dashDB
5. Technical White paper
IBM Analytics
5
Use cases
There are many ways that organizations are taking
advantage of dashDB. Listed below are four main use
cases that we are seeing dashDB customers active in today.
1. Augmenting the existing data warehouse – Hybrid
It is stated that over 90 percent of clients plan to augment
their existing data warehouse1
. dashDB is well structured
for this usage. Through dashDB, organizations are able to
extend on-premise data warehouse environments to the
cloud. Since you pay for the capacity you need today, the
platform is elastic and is there when you need it. With
dashDB, you are not forced into buying utility before
you need it.
2. Analysis of NoSQL data
The second key use case comes from core IBM
integration between Cloudant®
and dashDB. You can
easily synchronize your JSON data within Cloudant
to structured data within dashDB. This process then
allows for traditional BI and analytics common in data
warehouses. Since dashDB has in-database predictive
algorithms built in, those clients that are using Cloudant
can analyze their JSON document stores, hassle-free.
3. Data science data store
Specific to this idea of in-database analytics, the third
key use case is detailed here. For the statistically inclined
as well as for data scientists, dashDB maintains a robust
set of predictive analytic algorithms. As stated, dashDB
has R runtime and RStudio built in. R is widely used
among statisticians and data miners for developing
statistical software and data analysis.
4. Standalone data warehouse as a service
dashDB is heavily used as a standalone data warehouse
in the cloud. Whether it be small start-up datamarts
that you spawn off of some Cloudant data, a basic test
or development environment, or even full enterprise
data warehousing on the cloud, dashDB is available
24x7 for you and your organization.
Getting started with dashDB
IBM dashDB is a fully managed data warehousing service,
so there is no hardware or software to purchase and install.
Simply sign up for an account at www.dashdb.com and start
using it for free. If you need assistance learning dashDB or
determining whether it is a good fit for your organization’s
requirements, feel free to contact us online at dashdb.com,
and we will answer your questions or set up an in depth
discussion with our technical team.
About IBM dashDB solutions
IBM provides the most comprehensive portfolio of data
warehousing, information management and business
analytic software, hardware and solutions to help clients
maximize the value of their information assets and discover
new insights to make better and faster decisions and
optimize their business outcomes.
For more information
TTo learn more about dashDB, please contact your IBM
representative or IBM Business Partner, or visit the following
website: www.dashdb.com
Additionally, IBM Global Financing can help you acquire
the IT solutions that your business needs in the most
cost-effective and strategic way possible. We’ll partner
with credit-qualified clients to customize an IT financing
solution to suit your business goals, enable effective cash
management, and improve your total cost of ownership.
IBM Global Financing is your smartest choice to fund
critical IT investments and propel your business forward.
For more information, visit ibm.com/financing