The Ecosystem is too damn big

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DataWorks Summit/Hadoop Summit

The Ecosystem is too damn big

The Ecosystem
is Too Damn
Big
Andrew Brust
Senior Director, Market Strategy and Analysis
Datameer

there are too many vendors
Credit: Matt
Turck, Firstmark
Capital

ON‐PREMISES CLOUD
HADOOP AS A SERVICE OTHER
Cloudera
Hortonworks
MapR
IBM
Amazon Web Services
Microsoft Azure
Google Cloud Platform
Qubole
Altiscale
ArenaData
InfoSys
there are too many distributions

there are too many engines

there are too many projects

the problem with churn

problems
analysis paralysis
big bang platform decisions
one-size-fits-all imprecision
inevitable obsolescence
“engine choice dread”
on every single freakin’ job

too many...
SQL on Hadoop solutions
project iterations
vendor serving Apache projects
programming language choices

buzzwords
actionable insights
days instead of weeks
weeks instead of months
for the business user
data scientists
citizen data scientists
the 4th “V”
all your data in one place
data lake

MODULAR LOCKED
IN OR OUTPRODUCTS
LOOK FOR DON'T BE
NO
HARD CODING
IT'S ALL ABOUT
BALANCE
you'll need to fend for yourself

Top 6 Solutions

Industry:
build consortia
06

Vendors:
stop copying
each other’s
messages05

VCs:
stop enabling bad
marketing, funding
companies that
don’t add value04

Analyst firms:
decode things,
don’t confuse
them03

Technologists:
realize the
value of
standards02

Customers:
demand a consistent
core stack, do your
research, buy on the
merits01

thank you.andrew.brust@datameer
@andrewbrust

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There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases. In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.

The Challenge of Driving Business Value from the Analytics of Things (AOT)

The Challenge of Driving Business Value from the Analytics of Things (AOT)

The Challenge of Driving Business Value from the Analytics of Things (AOT)

DataWorks Summit/Hadoop Summit

In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.

Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop

Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop

Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop

DataWorks Summit/Hadoop Summit

In high-risk manufacturing industries, regulatory bodies stipulate continuous monitoring and documentation of critical product attributes and process parameters. On the other hand, sensor data coming from production processes can be used to gain deeper insights into optimization potentials. By establishing a central production data lake based on Hadoop and using Talend Data Fabric as a basis for a unified architecture, the German pharmaceutical company HERMES Arzneimittel was able to cater to compliance requirements as well as unlock new business opportunities, enabling use cases like predictive maintenance, predictive quality assurance or open world analytics. Learn how the Talend Data Fabric enabled HERMES Arzneimittel to become data-driven and transform Big Data projects from challenging, hard to maintain hand-coding jobs to repeatable, future-proof integration designs. Talend Data Fabric combines Talend products into a common set of powerful, easy-to-use tools for any integration style: real-time or batch, big data or master data management, on-premises or in the cloud.

From Regulatory Process Verification to Predictive Maintenance and Beyond wit...

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From Regulatory Process Verification to Predictive Maintenance and Beyond wit...

DataWorks Summit/Hadoop Summit

While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.

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Backup and Disaster Recovery in Hadoop

DataWorks Summit/Hadoop Summit

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HBase in Practice

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The Challenge of Driving Business Value from the Analytics of Things (AOT)

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Michael W. Hawkins

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

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With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

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This presentation explores the impact of HTML injection attacks on web applications, detailing how attackers exploit vulnerabilities to inject malicious code into web pages. Learn about the potential consequences of such attacks and discover effective mitigation strategies to protect your web applications from HTML injection vulnerabilities. for more information visit https://bostoninstituteofanalytics.org/category/cyber-security-ethical-hacking/

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HTML Injection Attacks: Impact and Mitigation Strategies

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The Ecosystem is too damn big

1. The Ecosystem is Too Damn Big Andrew Brust Senior Director, Market Strategy and Analysis Datameer

2. there are too many vendors Credit: Matt Turck, Firstmark Capital

3. ON‐PREMISES CLOUD HADOOP AS A SERVICE OTHER Cloudera Hortonworks MapR IBM Amazon Web Services Microsoft Azure Google Cloud Platform Qubole Altiscale ArenaData InfoSys there are too many distributions

4. there are too many engines

5. there are too many projects

6. the problem with churn

7. problems analysis paralysis big bang platform decisions one-size-fits-all imprecision inevitable obsolescence “engine choice dread” on every single freakin’ job

8. too many... SQL on Hadoop solutions project iterations vendor serving Apache projects programming language choices

9. buzzwords actionable insights days instead of weeks weeks instead of months for the business user data scientists citizen data scientists the 4th “V” all your data in one place data lake

10. MODULAR LOCKED IN OR OUTPRODUCTS LOOK FOR DON'T BE NO HARD CODING IT'S ALL ABOUT BALANCE you'll need to fend for yourself

11. Top 6 Solutions

12. Industry: build consortia 06

13. Vendors: stop copying each other’s messages05

14. VCs: stop enabling bad marketing, funding companies that don’t add value04

15. Analyst firms: decode things, don’t confuse them03

16. Technologists: realize the value of standards02

17. Customers: demand a consistent core stack, do your research, buy on the merits01

18. thank you.andrew.brust@datameer @andrewbrust