All business sizes can benefit from better use of their data to gain insights, how the cloud can help overcome common data challenges and accelerate transformation with the cloud technology
https://www.rapyder.com/cloud-data-analytics-services/
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
How In-memory Computing Drives IT SimplificationSAP Technology
Discover how the in-memory technology of SAP HANA can reduce complexity and simplify the IT landscape to foster real-time results, innovation and lower costs.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
Foundational Strategies for Trusted Data: Getting Your Data to the CloudPrecisely
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Cloud Data Management: The Future of Data Storage and ManagementFredReynolds2
Data is the essence of any business. It provides the organization, its people, and its customer’s timely and historical decision support. Data management’s importance must be considered. To maximize the benefits of cloud data management, businesses must first establish a mechanism for separating master data from other data types. Due diligence is required when choosing a data management platform and a data management system. Here, the potential of Cloud based Data Management emerges, enhancing the significance of these decisions.
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
How In-memory Computing Drives IT SimplificationSAP Technology
Discover how the in-memory technology of SAP HANA can reduce complexity and simplify the IT landscape to foster real-time results, innovation and lower costs.
Booz Allen Hamilton uses its Cloud Analytics Reference Architecture to build technology infrastructures that can withstand the weight of massive datasets – and deliver the deep insights organizations need to drive innovation.
Foundational Strategies for Trusted Data: Getting Your Data to the CloudPrecisely
To trust your reporting, analytics, and ML outcomes, you must have access to all the data required for confident decision-making. In this on-demand session we’ll explore strategies for breaking data out of silos and getting it into the cloud – with an emphasis on integrating data from complex legacy systems.
Cloud Data Management: The Future of Data Storage and ManagementFredReynolds2
Data is the essence of any business. It provides the organization, its people, and its customer’s timely and historical decision support. Data management’s importance must be considered. To maximize the benefits of cloud data management, businesses must first establish a mechanism for separating master data from other data types. Due diligence is required when choosing a data management platform and a data management system. Here, the potential of Cloud based Data Management emerges, enhancing the significance of these decisions.
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Foundational Strategies for Trusted Data: Getting Your Data to the CloudPrecisely
To trust your reporting, analytics, and ML outcomes, you must have access to all the data required for confident decision-making. In this on-demand session we’ll explore strategies for breaking data out of silos and getting it into the cloud – with an emphasis on integrating data from complex legacy systems.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
As technology advances, so does the data stack. Before you go into deploying a modern data stack at your company, here are some important things to know.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
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Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
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[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
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Securing the Future: A Guide to Cloud-Based Data ProtectionMaryJWilliams2
In an era where data breaches and cyber threats are increasingly common, cloud-based data protection emerges as a critical pillar for safeguarding digital assets. This article offers an in-depth exploration of cloud-based data protection strategies, tools, and best practices. Discover how leveraging the cloud can enhance your organization's data security posture, ensure business continuity, and provide scalability to meet future demands. To Know more: https://stonefly.com/white-papers/cloud-based-data-protection-strategies/
Capgemini Leap Data Transformation Framework with ClouderaCapgemini
https://www.capgemini.com/insights-data/data/leap-data-transformation-framework
The complexity of moving existing analytical services onto modern platforms like Cloudera can seem overwhelming. Capgemini’s Leap Data Transformation Framework helps clients by industrializing the entire process of bringing existing BI assets and capabilities to next-generation big data management platforms.
During this webinar, you will learn:
• The key drivers for industrializing your transformation to big data at all stages of the lifecycle – estimation, design, implementation, and testing
• How one of our largest clients reduced the transition to modern data architecture by over 30%
• How an end-to-end, fact-based transformation framework can deliver IT rationalization on top of big data architectures
Foundational Strategies for Trusted Data: Getting Your Data to the CloudPrecisely
To trust your reporting, analytics, and ML outcomes, you must have access to all the data required for confident decision-making. In this on-demand session we’ll explore strategies for breaking data out of silos and getting it into the cloud – with an emphasis on integrating data from complex legacy systems.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
As technology advances, so does the data stack. Before you go into deploying a modern data stack at your company, here are some important things to know.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
Evolving Big Data Strategies: Bringing Data Lake and Data Mesh Vision to LifeSG Analytics
The new data technologies, along with legacy infrastructure, are driving market-driven innovations like personalized offers, real-time alerts, and predictive maintenance. However, these technical additions - ranging from data lakes to analytics platforms to stream processing and data mesh —have increased the complexity of data architectures. They are significantly hampering the ongoing ability of an organization to deliver new capabilities while ensuring the integrity of artificial intelligence (AI) models. https://us.sganalytics.com/blog/evolving-big-data-strategies-with-data-lakehouses-and-data-mesh/
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
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Explore the essential strategies for safeguarding your data with cloud-based protection solutions. This comprehensive guide delves into the benefits of using cloud services for data security, including enhanced scalability, reliability, and disaster recovery capabilities. Learn about the latest trends, best practices, and how to effectively implement cloud-based data protection to ensure your data is secure, accessible, and recoverable. To Know more: https://stonefly.com/white-papers/cloud-based-data-protection-strategies/
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Capgemini Leap Data Transformation Framework with ClouderaCapgemini
https://www.capgemini.com/insights-data/data/leap-data-transformation-framework
The complexity of moving existing analytical services onto modern platforms like Cloudera can seem overwhelming. Capgemini’s Leap Data Transformation Framework helps clients by industrializing the entire process of bringing existing BI assets and capabilities to next-generation big data management platforms.
During this webinar, you will learn:
• The key drivers for industrializing your transformation to big data at all stages of the lifecycle – estimation, design, implementation, and testing
• How one of our largest clients reduced the transition to modern data architecture by over 30%
• How an end-to-end, fact-based transformation framework can deliver IT rationalization on top of big data architectures
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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).
Show drafts
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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.
2. Table of contents
2
3
4
6
8
10
11
13
14
15
16
17
Introduction
Limitations of do-it-yourself data
Modern data architectures
Accelerating actionable insights with the cloud
Assessment: How will cloud-based analytics transform your business?
Start anywhere and go anywhere with Amazon Web Services (AWS)
Take your first step
About Rapyder
How Rapyder helps its customers with Data Analytics solutions?
Case study: [Customer Name
Learn More
3. Introduction
3
This eBook is intended to help decision-makers in small and
medium-sized businesses understand how the cloud can help you
unlock the potential of becoming a data driven organization. Put
your data to work to make more informed decisions, improve
efficiencies, respond faster, and uncover opportunities. In this
eBook, you’ll learn why businesses of all sizes can benefit from
better use of their data to gain insights, how the cloud can help
overcome common data challenges and accelerate transformation,
how to evaluate whether your business would benefit from a
modern cloud-based analytics strategy, and what to look for in a
cloud service provider.
4. Limitations of do-it-yourself data
Data has become a competitive advantage, helping businesses anticipate and react to change. Processing large volumes
of data to identify unique insights used to be considered the domain of large companies with considerable resources and
technology investments. With access to better insights, small and medium sized organizations are also able to anticipate
market trends, improve operations, identify new customers, and increase sales.
Gartner defines analytics and business intelligence (ABI) as the applications, infrastructure, tools, and best practices that
enable access to and analysis of information to improve and optimize decisions and performance.1 Properly deployed,
analytics can deliver real-time insights to help organizations achieve business goals including, anticipating market trends,
improving operations, and identifying new customers.
It’s challenging to make data “analytics-ready,” often requiring more time, resources, and technical knowledge than you
have available. With data in multiple formats and locations, costs quickly add up and expertise becomes a limiting factor,
forcing businesses to fall back on spreadsheets and disconnected databases.
1. Gartner, “Gartner Information Technology Glossary.”
4
5. Disconnected data is often called a data silo—stored
data that can be accessed by one group but is
isolated from others in the same organization. Data
silos keep sales data from being accessible to
marketing, for example. Data silos can be caused by
an inability to link data coming from different sources,
like cloud services and information stored in purpose-
built company applications. Silos cause multiple
operational problems:
5
• The data needed for a given workload may be
split across multiple silos and is inaccessible.
• The silo where the data lives may not meet the
performance requirements for a given
workload.
• The silos may require different management,
security, and authorization approaches,
increasing operational cost and risk.
• Silos are not equipped to support the
exponential growth of event data like log files,
click stream data, and machine generated
data.
Further, analytics requires significant storage and
compute power, and that upfront investment can be
hard to justify, slowing progress.As a result,
organizations tend to get stuck with an ad hoc
approach, limiting the usefulness of the information
they can glean. It’s not surprising that some
businesses fall back on guesswork.
Using old or incomplete data can translate to
missing big opportunities. It can also contribute to
inefficient customer strategies and ineffective
marketing campaigns. According to IDC,
organizations with siloed data often don’t have the
ability to:
• Respond in the moment to new information
or predict future results
• Synthesize diverse internal and external data
sources into actionable information
• Get visibility into end-to-end business
processes and unified customer data for a
360-degree view
6. Modern data architectures
By breaking down data silos, you open the door to empowering capabilities that transform data into usable insights.
The best data strategies enable you to store any amount of data, at low cost, and in open, standards-based data formats.
IDC defines several elements of a modern data architecture:
1.Data warehouses in the cloud opportunistically consolidate data and provide a framework for basic analytics and
reporting. Data warehouses are often purpose-built for a specific type of data and provide immediate performance,
scale, and cost advantages over siloed data.
2.Data lakes in the cloud bring together data in various silos from internal and external sources and data in various
formats (structured or unstructured) to further enhance the quality of the of information gathered and increase the
relevance and repeatability of the analysis.
3.Integrated data platforms in the cloud consolidate and leverage all data sources to enable predictive analytics
through artificial intelligence (AI) and machine learning (ML) algorithms, fully capitalizing on all of the business benefits
of data-based decision making.
1. Gartner, “Gartner InformationTechnology Glossary.”
6
7. The process of collecting, cleaning, and consolidating data is just the beginning. Businesses need a data
strategy that enables them to put their vast amounts of data to work to make better, more informed
decisions, respond faster to the unexpected, predict what’s to come, improve efficiencies, and uncover
new opportunities. There are three elements to building out a modern data strategy:
1.Modernize your data infrastructure to be scalable and secure, including database provisioning,
patching, configuration, and backups. Organizations running on-premises data stores or self-
managing in the cloud have considerable overhead for management tasks such as database
provisioning, patching, configuration, and backups. The right cloud provider can automate and scale
to meet your needs reliably and securely.
2.Unify to make decisions more quickly by putting your data to work with secure and well-governed
access that will scale and grow as business needs change. You can connect diverse data sources
and locations, including your data lake, data warehouse, and all of the purpose-built data stores into
a coherent system that is secure and well governed.
3.Innovate to create new experiences and reimagine old processes to generate entirely new
revenue opportunities, make better and faster decisions, or improve operational efficiencies. Access
advanced machine learning and artificial intelligence services for builders of all levels of expertise.
A modern data architecture will allow you to connect your data into a coherent and cohesive whole. You
will be able to optimize for performance and cost, achieve unified data access, security and governance,
and use the latest analytics technologies.
7
8. Accelerating actionable insights
with the cloud
One of the most attractive advantages of the cloud for small and medium-sized businesses is that it provides access to the
same technologies and capabilities relied upon by larger competitors. The cloud was the number one IT priority in 2021 for
companies with fewer than 1,000 employees, according to TechAisle.2 And companies that subscribe to cloud services, on
average, say they save 31 percent using the cloud compared to running infrastructure on-site themselves.3
Data has become a competitive advantage, helping businesses anticipate and react to change. With the cloud, capturing,
storing, and analyzing all of your data is more achievable, more affordable, and more effective than ever. You can ramp
up and scale quickly and securely, getting business users access to the insights they need.
The cloud can help your business modernize, unify, and innovate, so you can:
•Increase efficiency. Get access to broad infrastructure options, including data lakes and purpose-built data stores.
Simplify data access across platforms and reduce time spent configuring drivers and managing database connections.
•Unify business intelligence. Make use of all of your data with secure access and governance. Build insight-driven
reports and dashboards across business intelligence tools. Enable your customers through embedded analytics.
•Reimagine your business. Built in machine learning and access to the latest AI technologies helps transform shared
data into trends and insights that allow you to make better decisions more quickly and pivot as business needs
change.
2. TechAisle, “2021 Top 10 SMB - Business Issues, IT Priorities, IT Challenges,” 2021.
3. AWS, “Accelerating your AWS Journey,” 2021.
8
9. Small and medium-sized businesses may believe their data isn’t “ready” or that a large investment is
required. You don’t have to build a team of experts on analytics infrastructure, make a large investment
in hardware and software, or figure out how to link disparate data on your own.
The cloud makes analytics:
•Accessible: Data users with various skill levels can readily access the right data to power
application development
and business insights.
•Actionable: Algorithms and machine learning systematically analyze your data quickly for timely
and useful insights.
•Affordable: Expenses are predictable so you can reduce the runaway costs of scaling
infrastructure as data continues to grow.
A cloud-based approach provides an affordable way to access the very latest tools to unlock your
analytics strategy. Using the cloud takes the complexity and cost out of building a modern data
architecture and opens new possibilities to empower business users with access to fresh, relevant
insights.
9
10. Assessment: How will cloud-based
analytics transform your business?
Every business has data generated from supplier, partner, and customer interactions that are stored and used in some way to
help inform decisions. The purpose of analytics is to shape disparate data into a framework for intelligent decision making to
drive the kind of competitive differentiation that can be transformational. Starting an assessment with infrastructure requirements
or concerns about the condition of your data is not uncommon, but the best test of the impact a cloud analytics strategy can
have on your business is to assess how it could help fulfill your business objectives. In the below assessment, check off which
factors are impacting your business to help assess whether moving to cloud-based analytics is right for you:
We often get surprised by changes in the market and would
like to be able to react faster.
Operational costs are increasing but it is difficult to pinpoint
where we could improve.
We need an easy way to build reports or consolidated
dashboards to share with management.
We have to rely on outdated information for forecasts and
other strategic activities.
We don’t have a way to easily connect to data that resides
in different sources and in different formats.
We can’t easily share data because of limitations in
managing data access permissions.
Users complain about the time it takes to run queries and
they aren’t able to self-serve.
We don’t feel comfortable relying on the data we can
access to predict market changes or anticipate business
needs.
Internal users are asking for different views of the data that
are time consuming to deliver or can’t be done currently.
If you checked any of the above, building analytics in the cloud could help you improve key business
outcomes and enhance the value business users extract from your data.
10
11. Start anywhere and go anywhere with
Amazon Web Services (AWS)
Moving to the cloud can help you get answers quickly instead of spending time building infrastructure and configuring analytics
services to work together. Working with an experienced cloud provider is an important part of a successful deployment.
Businesses of any size can benefit fromAWS with cloud storage, compute, and network infrastructure that meets the specific
needs of analytic workloads. AWS services provide a fully integrated analytics stack and a mature set of analytics tools
optimized for performance and cost.An IDC Study of AWS Data Lakes,Analytics and ML services, sampledAWS customers and
found that on average, they reduced total cost of operations by 48 percent, IT infrastructure by 42 percent, reduced time to run
queries by 79 percent, and increased the number of queries by 37 percent.4
AWS services help you modernize, unify, and innovate with:
•Access to the latest data architectures.
AWS provides the broadest selection of
analytics services from data movement, data
storage, data lakes, big data analytics, log
analytics, and streaming analytics. AWS
purpose-built services are designed to provide
the best price performance, scalability, and
lowest cost.
•Integration that accelerates time to insight.
AWS has native integration between all the
layers of the analytics stack enabling you to
quickly analyze data using any approach. We
manage the underlying infrastructure so you can
focus solely on your application.
•Affordable performance. AWS offers a self-tuning system
that delivers consistently fast results from gigabytes to
petabytes of data, and from a few users to thousands.
Resizing can be managed by AWS, and there is no limit to
how often, or by how much, a cluster can scale.
•Comprehensive platform for predictive analytics. AWS
offers a comprehensive platform of predictive and ML-
powered analytics, so you don’t have to build the expertise in
house.Artificial intelligence technologies can help you
uncover hidden insights and trends in your data, identify key
drivers, and forecast business metrics. Business users can
see root causes, improve forecast accuracy, evaluate risks,
and make better-informed decisions.
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12. AWS right-sizes the approach to help you meet you where you are in your analytics journey. You can start anywhere and go
anywhere with AWS, which offers businesses of your size:
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4. IDC & AWS, “The Business Value of AWS Data Lakes, Analytics and ML Services,” 2020.
5. Nucleus Research & AWS, “Guidebook: Understandingthe Value of Migrating from On-Premises to AWS for ApplicationSecurity and Performance,” June 2020.
Infrastructure built for analytics: AWS cloud
infrastructure is designed to scale storage resources to
meet fluctuating needs with maximum durability, protect
your data, and avoid the manual and time-consuming
tasks of setting up data warehouses or data lakes.
Infrastructure built for analytics means that loading data
from diverse sources, monitoring these data flows,
setting up partitions, turning on encryption and
managing keys, re-organizing data into columnar
format, and granting and auditing access can be done
in days not months.And once deployed, you can run
and scale analytics in seconds not minutes.
Built-in reliability and resiliency: WithAWS,
customers are able to achieve a 69 percent reduction in
unplanned downtime5 and our extensive investment in
global availability zones and redundant networks,
storage, and compute help ensure that you always have
access to your critical data and applications. In addition,
we bring experience and frameworks to ensure
business continuity, including dedicated teams and
partners who can provide on-demand expertise
and support.
Capacity and scalability that grows as you need it: AWS
automatically adjusts cloud capacity to meet demand, while
only charging for what you use, ensuring you have the space
to grow without paying for more than you need. We
constantly monitor activity to balance loads, scaling compute
power and storage up or down to meet fluctuations in
demand and reduce unnecessary costs, ensuring you always
have enough capacity and visibility into what you are
spending.
Support through best-in-class programs and partners:
AWS provides the broadest and deepest set of managed
services for data lakes and analytics, along with the largest
partner community to help you build virtually any data and
analytics application in the cloud. AWS Data and Analytics
Competency Partners have demonstrated success in helping
small and medium-sized businesses evaluate and use the
tools and best practices for collecting, storing, governing, and
analyzing data.
13. Take your first step
Our Gain Insights Program is specifically designed to provide you with a vision for how the cloud and AWS services can
help you achieve business outcomes through analytics. Live workshops focus on working backwards from your specific
business objectives, then designing a solution and crafting an implementation plan grounded in a measurable business case.
Comprehensive demos of data visualizations and dashboards, and easy-to-use migration services are additional ways you
can assess, design, and enable your analytics framework.AWS can also help connect you with a partner suited to your
needs that can implement your plans and ensure a smooth and rapid ramp.
Fast moving markets make the availability of current, real-time data the only way to stay competitive. Analytics can help you
make better informed decisions and accelerate your business. Data-driven insights can drive reduced operational costs,
improve the effectiveness of marketing, increase competitiveness, and get you to your goals faster. No matter the condition
of your data, where you are currently storing it, or how few people you have to administer it, the cloud can help you turn that
data into rich insights. AWS solutions are designed to simplify your transformation and help you collect, consolidate, and
clean your data so you can start identifying patterns and predicting outcomes to get ahead of the competition.
Contact us today for a free and fast assessment of your data analytics options.
Learn more about how you can turn your data into insights.
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14. About Rapyder
Rapyder, an innovative cloud-centric consultancy, is driven by a clear mission to enable enterprises to achieve their
business objectives by harnessing cutting-edge technologies. Our expertise lies in providing cloud-based Data analytics
solutions to address intricate challenges across diverse sectors.
With expertise in data analytics and AI and ML–based recommendations, Rapyder transforms businesses into success
stories. Rapyder believes it can play a potential role in identifying their competitive potential and pave the way for real-
time business intelligence by extracting meaningful information from vast, unorganized, complicated data.
We collaborate closely with customer to harness the potential of Data Analytics to enhance efficiency, precision, and
decision-making. Our team, comprising of data professionals, possesses the capabilities to offer Data Analytics in broad
areas such as:
Financial sectors
HR
Manufacturing & supply chain
Transportation & logistics, and many more.
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15. Rapyder’s Data Analytics services empower businesses to make data-driven decisions, enhance efficiency, improve
customer experiences, gain a competitive advantage, manage risks, optimize resources, drive innovation, and achieve
measurable results, ultimately leading to sustainable growth and success.
We transform businesses into success stories by assessing infrastructure, analyzing pain points, and conducting internal
research to prepare systems for AI implementation, minimizing downtime.
Some of the noted benefits of Data Analytics solutions are:
Informed Decision Making:
Data Analytics helps organizations make informed decisions based on data-driven insights. By analyzing patterns
and trends, businesses can understand customer behavior, market demands, and operational efficiency, leading to
better strategic decisions.
Improved Efficiency:
Analyzing data allows businesses to identify inefficiencies in their processes. By optimizing these processes,
companies can streamline their operations, reduce costs, and improve overall efficiency.
Enhanced Customer Experience:
Data Analytics enables businesses to gain a deeper understanding of customer preferences and behavior. This
insight can be used to personalize products, services, and marketing efforts, leading to a better customer
experience and increased customer satisfaction.
How Rapyder helps its customers with
Data Analytics solutions?
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16. Human Contact and Mistakes were reduced
with Sagemaker & MLOps
PayMe is the best personal loan
app for online loans. The loan
amount is disbursed to the bank
with the least amount of
paperwork possible. The loan
approval system uses machine
learning models, which predict
whether a user is qualified to
receive a loan based on the
user’s criteria.
PayMe
Challenge
PayMewantedanAWSmachinelearning solution to help their fintech company flourish. They aim to
make use of existing AI power. The main objective is to improve the model’s accuracy and use
the AWS cloud with its low cost, high performance, monitoring, and scalability. This company has
already implemented a machine learning model, but as time passes, data grows, and older
machine learning models are not accurate and scalable.
Solution
AWS Sagemaker was suggested since it is an extremely dependable managed solution for
keeping machine learning workloads in AWS.
In accordance with the best practices, a distinct network was built using a combination of
VPC and Subnets.
Sagemaker was introduced in a secure subnet.
Data and model artifacts would be kept on Amazon S3.
Exploratory data analysis, data visualization, data processing model training, evaluation, and
Sagemaker pipeline creation would all be done using Jupyter Notebooks based on Amazon
Sagemaker.
Investigating various algorithms using Sagemaker model training and evaluation to discover
the best algorithm for the problem.
Creating the Sagemaker pipeline based on the code for the model training, evaluation, and
deployment that has been finalized.
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17. AWS Sagemaker offered centralized solutions for all business needs related to machine learning,
including interactive notebooks, powerful instances, model monitoring, and automatic re-training.
Sagemaker MLOps decreased the human interaction labor needed for model re-training and
monitoring since it was now performed automatically.
By using these robust machine learning-based solutions, the model can now approve or reject loans
based on the information provided by the consumer, reducing human contact and mistake.
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18. Learn more
Business Intelligence for Small and Medium-sized Businesses
Data Analytics Assessment
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