Big Data LDN 2020

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Big Data LDN 2020

Author: Big Data LDN

Big Data LDN (London) is the UK’s largest free to attend data & analytics conference and exhibition, hosting leading data and analytics experts, ready to arm you with the... Read more

What You Will Learn

  • Data Analytics
  • Big Data
  • Data Science
  • DataOps
  • Artificial Intelligence (AI)
  • Data Governance

Pre-requisites

None.

Keynotes

Put Your Machine Learning on Autopilot

by Big Data LDN

Oct 27, 2020 / 36m

36m

Start Course
Description

A typical machine learning workflow consists of many steps including data analysis, feature engineering, model training and model tuning. What if our machine learning platform could perform these tasks for us and generate high-quality model candidates ready for review and deployment? In this session, you will learn about Automated Machine Learning (AutoML) and how the latest advances in AutoML allow you to put your machine learning models into autopilot mode while maintaining full visibility and control. There will also be an AutoML demonstration using Amazon SageMaker Autopilot, a fully managed AutoML service offered by AWS.

Table of contents
  1. Put Your Machine Learning on Autopilot

Who is Afraid of the Big Bad Data Wolf?

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

Just like the classic children’s fable of “The Three Little Pigs”, data professionals can feel like they are constantly building data houses in the form of new infrastructure and technology. However, when the full force of data blows in, these houses reveal themselves to be made of straw or sticks (like the first two pigs in the fable) and will get blown down under the pressure. This fear extends beyond data teams to business users who become afraid to touch anything in the data house for fear of breaking something or doing something wrong. This session will explore fears around the use of data and how strategic data governance can give people the confidence to be curious and explore data.

Table of contents
  1. Who is Afraid of the Big Bad Data Wolf?

AI & Data Science

What I've Learned from Advising 400+ Data Science Projects

by Big Data LDN

Oct 27, 2020 / 36m

36m

Start Course
Description

Throughout Alice Zhao's career, she's had the opportunity to meet and mentor many data scientists. Along the way, Alice has learned a few things, like the difference between a good and a great data scientist, some data science personas to strive for and those to avoid, and what companies are starting to do to hire the best talent. During this session, she'll share stories of experiences that have led to some surprising findings.

Table of contents
  1. What I've Learned from Advising 400+ Data Science Projects

Practical Digital Transformation: Through the Systematic Use of Data and Models

by Big Data LDN

Oct 27, 2020 / 28m

28m

Start Course
Description

Organizations with digital transformation initiatives are making the shift from visionary ambitions to practical projects. These organizations have defined their high-level digital transformation objectives and are now looking to their engineers and scientists to achieve them. This will involve learning new technologies, collaborating with unfamiliar groups, and proposing new products and services. To meet this challenge, technical organizations must master how to systematically use data and models, not only during the research and development stages, but also across groups throughout the lifecycle of the offering. An effective digital transformation plan needs to consider changes in people’s skills, processes, and technology. Join this session as Michael Carone from MathWorks describes this pragmatic approach to digital transformation and demonstrates how engineering and scientific teams are leveraging data and models to achieve their digital transformation objectives.

Table of contents
  1. Practical Digital Transformation: Through the Systematic Use of Data and Models

Best Practices for Building AI in a Responsible Way

by Big Data LDN

Oct 27, 2020 / 29m

29m

Start Course
Description

As the AI industry continues to mature and more companies begin to adopt this powerful technology, it is important for professionals to start considering how to build their AI journey in a responsible manner. Responsible AI covers areas like explainable AI, ethical AI, risk in AI, and more. All of these areas can have a major impact on businesses who are starting their AI journey. Join Harib Bakhshi, Lead Data Scientist for UK and Ireland at H2O.ai, who will walk you through some of the best practices around responsible AI. As part of this session, Harib will cover: A short overview of why Responsible AI is an important aspect to take into account, the business benefits of implementing responsible AI, and some practical tips to consider while going through your responsible AI journey.

Table of contents
  1. Best Practices for Building AI in a Responsible Way

Datarobot MLOps - The Last Step from Data to Value

by Big Data LDN

Oct 27, 2020 / 18m

18m

Start Course
Description

The share of AI models created but never put into production at large enterprises has been estimated to be as high as 90% or greater. With massive investments in data science teams, platforms, and infrastructure, the number of undeployed AI projects is dramatically increasing — along with the number of missed opportunities. Unfortunately, most projects are not showing the value that business leaders expect and are introducing new risks. DataRobot MLOps delivers the capabilities Data Science and IT Ops teams need to work together to deploy, monitor, manage, and govern machine learning models in production. With DataRobot MLOps, data scientists can: Deploy models in hours not months, proactively monitor ML model health, make efficient and trusted model updates, and apply ML governance best practices.

Table of contents
  1. Datarobot MLOps - The Last Step from Data to Value

AI Ethics: Understanding Bias and Fairness in Your Models

by Big Data LDN

Oct 27, 2020 / 30m

30m

Start Course
Description

As machine learning and artificial intelligence (AI) usher in the Fourth Industrial Revolution, it seems like everyone wants to get in on the action. And who can blame them? However, just like humans, AI systems are not exempt from making unfair decisions; sometimes this comes with serious consequences. This talk will cover how to appropriately identify and measure model bias and fairness, how to understand potential sources of bias and finally how to mitigate bias.

Table of contents
  1. AI Ethics: Understanding Bias and Fairness in Your Models

Doubling the Performance of AI for Fraud Detection with Graph

by Big Data LDN

Oct 27, 2020 / 43m

43m

Start Course
Description

Using TigerGraph and some Python machine-learning, we show you how anyone can build a payment fraud system that halves the number of frauds and reduces the number of inappropriately blocked transactions. Richard will cover the entire stack in this demonstration, including data, architecture and code.

Table of contents
  1. Doubling the Performance of AI for Fraud Detection with Graph

The Future of Work: Augmenting Human Intelligence with AI

by Big Data LDN

Oct 27, 2020 / 24m

24m

Start Course
Description

The future of work is a concept that’s now consistently bandied about in all circles. How can organisations get the best out of their brightest team? And how can the best employees get the most out of work? The answer to these commonly asked questions might be one in the same—augmented intelligence. In this talk, you will discover how organisations and workers can realise the benefits of intelligent automation and how it is possible to have greater access to control of process while reducing complexity, uncovering critical insights among growing volumes of unstructured data.

Table of contents
  1. The Future of Work: Augmenting Human Intelligence with AI

Mind the Gap: Uniting Technical and Business Expertise to Deliver Profound Transformation with ML

by Big Data LDN

Oct 27, 2020 / 27m

27m

Start Course
Description

Recent high-profile controversies around the user impact of biased or faulty AI have exposed common flaws at the design stage. In this session, Katie will explore the potential pitfalls of ML design and how to avoid them, and will showcase the importance of a human-centric approach to building an ethical and long-lasting AI strategy.

Table of contents
  1. Mind the Gap: Uniting Technical and Business Expertise to Deliver Profound Transformation with ML

BI & Analytics

National Trust: Transforming Our Data & Reporting Landscape

by Big Data LDN

Oct 27, 2020 / 21m

21m

Start Course
Description

In this session, hear from National Trust about their data and reporting transformation.

Table of contents
  1. National Trust: Transforming Our Data & Reporting Landscape

Data Visualization Best Practices

by Big Data LDN

Oct 27, 2020 / 25m

25m

Start Course
Description

Learn about telling stories with data and visual best practices with Kate Strachnyi. Humans are better at processing images as compared to written or spoken information. By using data visualisations, your audience can learn quickly and you can tell an impactful story in a business environment. Creating effective data visualisations can be challenging, given the sheer quantity of available data and the numerous methods of visualising it. Kate Strachnyi will provide you with data visualisation best practices that you can apply to your work today!

Table of contents
  1. Data Visualization Best Practices

How Meteomatics' Weather API Improves Decision Making, Realizes Efficiencies, and Saves Costs

by Big Data LDN

Oct 27, 2020 / 34m

34m

Start Course
Description

In this session Dr. Martin Fengler, CEO & Founder of Meteomatics, explains how Meteomatics' weather API works, what its advantages are and how it can significantly improve your business. During the live demonstration, you get a closer look at how fast, accurate and simple the weather API is.

Table of contents
  1. How Meteomatics' Weather API Improves Decision Making, Realizes Efficiencies, and Saves Costs

Data-driven

Architecting the Global Real-time Fraud Prevention with a Performant Data Platform

by Big Data LDN

Oct 27, 2020 / 36m

36m

Start Course
Description

Creating the world’s largest digital identity platform requires decisioning based on analysis of data from a number of global sources in real-time. Any digital business, regardless of industry, depends on speed and efficiency to drive operational decisions. Making faster, accurate, and real-time customer trust decisions removes friction and delivers superior business outcomes.

In this session, Nick Blievers, VP Engineering at ThreatMetrix®, will discuss how: Risk-based authentication leveraging digital identities is key to empowering customer transactions, real-time customer trust decisions can reduce fraud and improve customer satisfaction, selecting the right high performance data platform can improve decisioning and avoid spiralling complexity, and machine learning is powered at the data layer.

Table of contents
  1. Architecting the Global Real-time Fraud Prevention with a Performant Data Platform

Transforming the Delivery of Government Services with a Citizen Digital Identity

by Big Data LDN

Oct 27, 2020 / 23m

23m

Start Course
Description

Imagine a world where technology, process and organisational expertise can make a real contribution to the pressures faced by governments. In today’s world, there is a need to improve and expand how governments serve their citizens and ensure that those entitled to receive government services (and only those) are able to access them easily and efficiently - a transformation roadmap with biometric identity management at its heart.

Table of contents
  1. Transforming the Delivery of Government Services with a Citizen Digital Identity

Data at HSBC Wealth and Personal Banking

by Big Data LDN

Oct 27, 2020 / 29m

29m

Start Course
Description

During this fireside chat, Ranil Boteju, Global Head of Data Analytics at HSBC Wealth and Personal Banking, discusses the role data plays in ensuring the highest quality of service for the bank's customers. He also talks about the kind of skills and teams required to leverage data at scale and the advantages and disadvantages of being one of the worlds largest and most respected brands.

Table of contents
  1. Data at HSBC Wealth and Personal Banking

DataOps

From Tables to Documents - Changing Your Database Mindset

by Big Data LDN

Oct 27, 2020 / 41m

41m

Start Course
Description

Did you grow up on SQL databases? Are document databases a bit of a mystery to you? This is the session for you! We’ll compare terms and concepts, explain the benefits of document databases, and walk through the 3 key ways you need to change your mindset to successfully use document databases.

Table of contents
  1. From Tables to Documents - Changing Your Database Mindset

Enterprise DataOps: Continuous Component Based Development of Trusted, Reusable Data Assets

by Big Data LDN

Oct 27, 2020 / 56m

56m

Start Course
Description

As the data landscape becomes more complex with many new data sources, and data spread across data centres, cloud storage and multiple types of data store, the challenge of governing and integrating data gets progressively harder. The question is what can we do about it? This session looks at how a data lake, data fabric and data catalog software can be used to connect to and discover data across a distributed data landscape and enable continuous, component based development of data analytics pipelines to produce trusted re-usable data assets. This is otherwise known as Enterprise DataOps.

Table of contents
  1. Enterprise DataOps: Continuous Component Based Development of Trusted, Reusable Data Assets

ETL vs. ELT: Who Cares?

by Big Data LDN

Oct 27, 2020 / 44m

44m

Start Course
Description

A modern Cloud Data Architecture has fundamentally changed the approach and philosophy of integrating your data into a warehouse. The traditional ETL approach was synonymous with on-premise solutions that could handle fixed interfaces into your core systems. Now, with the explosion of data, we need a new approach to import and transform structured / semi-structured data feeds which can reduce the effort but also perform & scale as your business grows. In this session, we will explore why ELT is the key to taking advantage of Cloud Data Architecture and give IT and your business the approach and insight that can be discovered from your companies greatest asset – your data.

Table of contents
  1. ETL vs. ELT: Who Cares?

Data Transformation

What's for Dinner? Meaningful Choice through Data Science

by Big Data LDN

Oct 27, 2020 / 30m

30m

Start Course
Description

In this session, we will discuss how recipe kit delivery company Gousto is using Data Science to offer meaningful choice to our customers. To achieve this, we use a combination of optimising the set of recipes appearing on the weekly menu and personalised recommendations. We will discuss how the different systems work together and what the future might bring!

Table of contents
  1. What's for Dinner? Meaningful Choice through Data Science

10 Practical Tools for Data Science

by Big Data LDN

Oct 27, 2020 / 30m

30m

Start Course
Description

Common tools like Pandas, Numpy, and Sklearn are often used to help Data Scientists get things done, but there are lots of great Open Source libraries and product management tools Data Scientists can use to help accelerate their work. Join Sophia Goldberg to learn about 10 tools for data science that she discovered this year.

Table of contents
  1. 10 Practical Tools for Data Science

The 3Ts of a People-first Organization

by Big Data LDN

Oct 27, 2020 / 33m

33m

Start Course
Description

This session will cover how Trust, Transparency, and Tempo define a successful organisation.

Table of contents
  1. The 3Ts of a People-first Organization

Data Visionaries

3 Must-have Artifacts for the Data Driven Organisation

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

Here's an overview of 3 important tools any organisation should be using. In this webinar you will learn what a business glossary, a data dictionary, and a data catalog are, the benefits they bring, and what the relationship between all 3 is.

Table of contents
  1. 3 Must-have Artifacts for the Data Driven Organisation

Fast Data

Flink SQL: From Real-time Pattern Detection to Online View Maintenance

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

As stream processing becomes the standard for real-time analytics and event-driven applications, the pressure to efficiently derive insights from an ever-growing amount of data is unparalleled. While we have more powerful tools than ever to build scalable and reliable data pipelines, the time and expertise required to master them is significant. SQL is making a comeback as a way to lower the entry barrier of this fast-paced data world, making the inherent potential of streaming data more accessible and more broadly used across organizations. Flink SQL is a high-level abstraction that is widely used to power business-critical applications at companies like Alibaba, Yelp, Uber or Huawei. In this talk, we'll explore the basics of Flink SQL and showcase how you can easily build and deploy analytical applications ranging from real-time pattern detection to online view maintenance.

Table of contents
  1. Flink SQL: From Real-time Pattern Detection to Online View Maintenance

Machine Learning & Apache Kafka: Bringing Intelligent Software to the Next Level

by Big Data LDN

Oct 27, 2020 / 24m

24m

Start Course
Description

Machine Learning (ML) is separated into model training and model inference. ML frameworks typically use a data lake like HDFS or S3 to process historical data and train analytic models. But it’s possible to completely avoid such a data store, using a modern streaming architecture. This talk compares a modern streaming architecture to traditional batch and big data alternatives and benefits like the simplified architecture, the ability to reprocess events in the same order for training different models, and the possibility to build a scalable, mission-critical ML architecture for real-time predictions. The talk explains how this can be achieved by leveraging Apache Kafka, Tiered Storage, and TensorFlow.

Table of contents
  1. Machine Learning & Apache Kafka: Bringing Intelligent Software to the Next Level

Microservices Patterns with Streaming

by Big Data LDN

Oct 27, 2020 / 20m

20m

Start Course
Description

With Industry 4.0, several technologies are used to have data analysis in real-time. Maintaining, organising, and building this, on the other hand, is a complex and complicated job. Over the past 30 years, we saw several ideas to centralise the database in a single place, as the true source of data, have been implemented in companies, such as Data Warehouse, NoSQL, Data Lake, Lambda & Kappa Architecture. On the other hand, Software Engineering has been applying ideas to separate applications to facilitate and improve application performance, such as microservices. The the idea to use microservice patterns on the data and divide the model into several smaller ones using the DDD principles.

Table of contents
  1. Microservices Patterns with Streaming

Introduction to Data Streaming

by Big Data LDN

Oct 27, 2020 / 44m

44m

Start Course
Description

While “software is eating the world,” those who are able to best manage the huge mass of data will emerge out on the top. The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available. In this talk, Nicolas Frankel will define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, explore pros and cons, and outline existing technologies implementing the latter with their most prominent characteristics. Nicolas will conclude by describing one possible use-case of data streaming that is not possible with batches.

Table of contents
  1. Introduction to Data Streaming

Apache Kafka and KSQLDb in Action: Let's Build a Streaming Data Pipeline

by Big Data LDN

Oct 27, 2020 / 29m

29m

Start Course
Description

Have you ever thought that you needed to be a programmer to do stream processing and build streaming data pipelines? Think again! Apache Kafka is a distributed, scalable, and fault-tolerant streaming platform, providing low-latency pub-sub messaging coupled with native storage and stream processing capabilities. Integrating Kafka with RDBMS, NoSQL, and object stores is simple with Kafka Connect, which is part of Apache Kafka. ksqlDB is the event streaming database for Apache Kafka, and makes it possible to build stream processing applications at scale, written using a familiar SQL interface. In this talk, we’ll build a streaming data pipeline using nothing but our bare hands, Kafka Connect, and ksqlDB. Gasp as we filter events in real-time! Be amazed at how we can enrich streams of data with data from RDBMS! Be astonished at the power of streaming aggregates for anomaly detection!

Table of contents
  1. Apache Kafka and KSQLDb in Action: Let's Build a Streaming Data Pipeline

Governance

The Dangers of Dirty Data and How to Fix It

by Big Data LDN

Oct 27, 2020 / 36m

36m

Start Course
Description

We all think we know what bad data looks like, but what is it and what are the consequences? Susan Walsh, The Classification Guru, has spent nearly a decade classifying, normalising and cleansing spend data and will share real-life examples of dirty data, and the consequences it has on the output, such as decision making, reporting, analytics, AI and machine learning. She will share how to make quick, accurate checks and changes to your own data in Excel, regardless of your level of experience, explain why data accuracy and maintenance is so important, and implement best practices for this. Takeaways include: The importance of data accuracy, the consequences of dirty data, how to ensure data accuracy, knowing how to make sure your data has its COAT on, and how to spot check your data.

Table of contents
  1. The Dangers of Dirty Data and How to Fix It

(Y)our Data Journey

by Big Data LDN

Oct 27, 2020 / 26m

26m

Start Course
Description

In this session, Alasdair Moore will outline the data journey we're all on, and why it's so exciting

Table of contents
  1. (Y)our Data Journey

AI vs. Pandemic - The Fine Line Between Saving Lives and Privacy Intrusion

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

Artificial Intelligence has a strategic role in fighting the current COVID-19 pandemic by identifying possible new outbreaks, automatically identifying high-risk areas and by detecting recent close contacts of infected individuals. However, AI processes large quantities of personal data. When algorithms process personal data categories like race, gender, age etc, the risks of discrimination against communities are huge. In this session, we will explore the fine lines between saving lives and respecting the rights of individuals, assessing risks related to big data processing operations, finding legal grounds and taking the right measures to limit the risks to human rights while efficiently fight the spread of COVID-19.

Table of contents
  1. AI vs. Pandemic - The Fine Line Between Saving Lives and Privacy Intrusion

Winning Friends and Influencing People for Successful Data Governance

by Big Data LDN

Oct 27, 2020 / 27m

27m

Start Course
Description

To be successful at Data Governance you need to invest a lot of effort in managing and influencing stakeholders in your organisation at all levels. In this session, Nicola will share tips and approaches to help you be successful in your Data Governance initiative.

Table of contents
  1. Winning Friends and Influencing People for Successful Data Governance

Unlocking Unstructured: Leveraging Data Discovery

by Big Data LDN

Oct 27, 2020 / 37m

37m

Start Course
Description

Whether you are building out an analytics program or maturing your data management process, the first step begins with finding and understanding the data that is available in your organization. The integrity of your data initiatives begins with knowing that you are using the right data. Learn how ML-based data discovery transforms data quality, compliance, and privacy initiatives - adding context, enhancing integrity, and enabling data management organizations to improve their data coverage. We will share use cases and customer success stories that benefited from leveraging an in-depth discovery tool to gain insights on their entire structured and unstructured data landscape.

Table of contents
  1. Unlocking Unstructured: Leveraging Data Discovery

Empowering People to Unleash the Potential of Data: The Case for Data Literacy

by Big Data LDN

Oct 27, 2020 / 40m

40m

Start Course
Description

The challenge of data literacy is manifold: How do we define it? How can we measure it? And, if there is a competency gap, then how can we improve literacy? In search for answers, we explore the multiple dimensions of data literacy. Beyond the capability to read and interpret data, it is also about critical skills, fact-checking, sourcing data, understanding bias, visualization, etc. We also discuss an open-source framework that would allow measuring data literacy for individuals, organizations, and society so that data can unleash its potential for all.

Table of contents
  1. Empowering People to Unleash the Potential of Data: The Case for Data Literacy

A Look at Data from a Security Perspective: What Data Is Valuable to Outsiders and Why

by Big Data LDN

Oct 27, 2020 / 35m

35m

Start Course
Description

Join Rik Fergusion to better understand which data is valuable.

Table of contents
  1. A Look at Data from a Security Perspective: What Data Is Valuable to Outsiders and Why

Data Governance - Why Bother?

by Big Data LDN

Oct 27, 2020 / 32m

32m

Start Course
Description

Let’s face it, data governance is not the sexiest aspect of becoming a data-driven organisation, but, without it, many organisations can’t maximise the value that they derive from their data, whilst minimising business risk. In this short session, we'll focus on Master Data, Reference Data and Metadata Management and will walk through a number of scenarios to illustrate how they solve real-world problems that could be holding your business back and exposing it to risk. The session is aimed at senior management who wish to understand the business benefits that data governance can bring to their organisation and for those who need to justify embarking on this journey.

Table of contents
  1. Data Governance - Why Bother?

Modern Data Architecture

Imposter Syndrome in the IT World from a Conference Speaker's Perspective

by Big Data LDN

Oct 27, 2020 / 28m

28m

Start Course
Description

Have you ever compared yourself with other team members and felt like a fraud? Have you ever felt unworthy of your job promotion? Have you ever doubted your successes? Do you know someone who could have answered "Yes" to any of those questions? These are symptoms of Imposter Syndrome, which affects many people working in IT. In this session, Antonio Cobo Cuenco shares his struggles in public speaking and how he fights imposter syndrome at every conference.

Table of contents
  1. Imposter Syndrome in the IT World from a Conference Speaker's Perspective

Deployment Isn't the Final Step: Monitoring Machine Learning Models in Production Environments

by Big Data LDN

Oct 27, 2020 / 27m

27m

Start Course
Description

Whether it is auto-translation, auto-completion, face or voice recognition, recommendation systems or autonomous driving, AI-based systems can be found in almost every aspect of our daily lives. Although the development of learning systems has become common among companies and a number of methodologies have been developed around them, there is still a lot of confusion around the deployment of those systems in a production environment - whose responsibility it is and most importantly who monitors those models once they are deployed. In this session, we will talk about the data science project cycle which holds five main stages - defining your project objectives, collecting and cleaning your data, training and testing a predictive model, deploying it in a production environment and monitoring its actions and decisions. We will then concentrate on the last forgotten stage, which is critical for DevSecOps teams, and see why monitoring those systems is crucial for organizations using real-life examples from recent years of AI-based systems that went crazy when they were deployed without any supervision.

Table of contents
  1. Deployment Isn't the Final Step: Monitoring Machine Learning Models in Production Environments

Exploring and Visualising Data with ElasticSearch for Data Scientists

by Big Data LDN

Oct 27, 2020 / 39m

39m

Start Course
Description

Elasticsearch is just another datastore.. or is it? Come and find out. With Kibana’s visualisation abilities and Elasticsearch’s increasing popularity, it’s increasingly likely you’ll use the datastore soon. Come to this practical session and learn how to talk to Elasticsearch from a Jupyter notebook and quickly explore datasets with Kibana’s visualisations and maps.

Table of contents
  1. Exploring and Visualising Data with ElasticSearch for Data Scientists

Now That We've Got Big Data, What Are We Going to Do with It?

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

In this session, you'll discover: How enterprises are maximising the use of their analytics insights by moving from ETL, batch and micro-batch to event driven architectures to generate more insights, more quickly, with more value; how industries from finance to manufacturing to supply chain are adopting concepts such as deferred execution, choreography and publish/subscribe to increase customer satisfaction and drive down costs; the latest tools, techniques and methodologies to help adopt event driven architectures while liberating data from legacy systems; and how hybrid- and multi-cloud deployments can be enabled at the same time.

Table of contents
  1. Now That We've Got Big Data, What Are We Going to Do with It?

Analytics Acceleration for Data Lakes

by Big Data LDN

Oct 27, 2020 / 32m

32m

Start Course
Description

Accelerating analytics on existing data lake infrastructure requires a database with scalable rapid data ingestion and fast queries of large data sets leveraging the simplicity of SQL. We will be exploring The Database of Now from MemSQL and show how it provides real-time analytic performance across several data sources with scalable SQL for an integrated hybrid platform.

Table of contents
  1. Analytics Acceleration for Data Lakes

Powering the Data Lakehouse with Databricks

by Big Data LDN

Oct 27, 2020 / 45m

45m

Start Course
Description

We've seen a huge shift in popularity from Data Warehouses to Data Lakes, but, often, we still use both. Databricks is leading the charge in a new data paradigm they call the "Data Lakehouse" that looks to build a platform using the best features of both. This utilises the open source Delta Lake, or the premium Delta on Databricks. This session will take a deeper look at some of the problems this approach is targeting, the tools & functionality that Delta provides and how we can bring it all together as a single, unified analytics platform. If you’re planning, currently building, or looking after a Data Lake with Spark and want to get to the next level of performance and functionality, this session is for you.

Table of contents
  1. Powering the Data Lakehouse with Databricks

Modern Data Architecture at a Challenger Bank: Fireside Chat with Jason Maude, Starling Bank

by Big Data LDN

Oct 27, 2020 / 31m

31m

Start Course
Description

In this session, Jason Maude from Starling Bank answers questions posed by Big Data LDN about why it is now possible for the challenger banks to be on a level playing field with the big incumbent banks thanks to advances in technology and modern team structures and processes, which allow them to offer a far more flexible and customer focussed service than was available to customers previously.

Table of contents
  1. Modern Data Architecture at a Challenger Bank: Fireside Chat with Jason Maude, Starling Bank

Architecting Production IoT Analytics

by Big Data LDN

Oct 27, 2020 / 32m

32m

Start Course
Description

What works in production is the only technology criteria that matters. When you look at technology and data engineering choices, even in companies with wildly different Internet of Things (IoT) use cases, you see something surprising: Successful production IoT architectures show a remarkable number of similarities. Analysing Internet of Things data has broad applications in a variety of industries from smart buildings to smart farming, from network optimisation for telecoms to preventative maintenance on expensive medical machines or factory robots. Among other things, every successful IoT architecture includes Kafka. Join us as we drill into the data architectures in a selection of companies like Philips, Anritsu, and Optimal+. Each company, regardless of industry or use case, has one thing in common: Highly successful IoT analytics programs in large scale enterprise production.

Table of contents
  1. Architecting Production IoT Analytics