Talend provides the data agility businesses need to use the latest cloud technologies to act with insight across their organization and win in an economy being deeply transformed by exploding data volumes, technology innovation, and fundamental changes to the IT infrastructure. Join us to learn how Talend and Qubole together help companies’ business users execute data preparation workloads in the cloud at a fraction of the cost and resources.Read more >
In the last few years, Machine Learning has quickly gone from a niche subject to one with significant relevance to many companies and organizations. Across industries ranging from pharmaceuticals and healthcare, to retail and financial services, Machine Learning has become more widely used for solving new business requirements. But just what is Machine Learning and how does it work? Just how do you teach a machine to learn?
Watch the Webinar on Demand and get an overview of the following methods:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
This session is an Intermediate level talk. It is geared towards Architects, Data Scientists, Developers, Software Engineers and anyone with an interest in Data Matching.
Critical inhibitors and best practices for data lake success
What critical inhibitors put your data lake project most at risk? Learn why data lakes fail and how to achieve success using the power of cloud.
It's no surprise that massive amounts of unstructured and semi-structured data provide a wealth of information about the state of your business. Clickstreams, sensors, social media, and weblogs generate huge datasets that can be analyzed in a data lake, as long as that data lake is part of a scalable big data analytics infrastructure.
In this session, we show you how to put your data lake on the path to success. Topics include:
- Why data lakes fail
- Key enablers of a successful data lake project
- Agile DevOps practices for faster time-to-market
- Why a multi-cloud strategy is critical
- Incorporating governance and security into your data lake
Is Your Data Ready for GDPR?
As the deadline for GDPR approaches, it is time to get practical about protecting personal data.
We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.
A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.
Watch, as experts from MAPR and Talend show you how to:
- Diagnose the maturity of your GDPR compliance;
- Set up milestones and priorities to reach compliance;
- Create a foundation to manage personal data through a data lake;
- Master compliance operations - from data inventory to data transfers to individual rights management.
In this on-demand webinar, Google and Talend experts demonstrate how to implement machine learning algorithms into analytics pipelines, and extract sentiment analysis to achieve a new level of insight and opportunity.Read more >
Listen to our interview at Big Data LDN with Patrick Booth, VP at Talend UK.
Patrick will discuss the following:
- When it comes to big data, what do you see as the pros and cons of vendors adopting a new generation approach based on ‘open source’ compared to a legacy proprietary approach?
-What are the main challenges vendors face in taking advantage of new big data trends like Spark streaming, for example, to deliver business benefits?
-How easy is it for vendors to innovate in the open source environment?
-What are the biggest issues you see customers facing in terms of adopting new big data technologies and what are the main challenges vendors have in addressing these barriers?
- In terms of the management of big data and data integration in general, how important is it that organisations are able to leverage an agile development environment?
Talend and MapR break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.Read more >
Sales operations leaders from two of the fastest growing companies, Splunk and Talend, have teamed up to share their secrets on how they're scaling sales processes, improving sales performance and growing at rapid speeds using "SuperForecasting" methodology. Learn from these early adopters on the technologies they are embracing to drive better results.Read more >
Join data integration expert Darren Brunt for a unique perspective on the benefits and implementation of machine learning.
Explore what machine learning is, and why it's important, how machine learning is different from traditional programming, and what it takes to enable machines to learn.
Hadoop has become unavoidable. Companies of all sizes are at different stages of their thoughts on Big Data. Whether you're just starting to explore the platform or you already have several existing clusters, everyone faces the same challenge - to develop its internal expertise.
Specialists of Big Data, Talend, and Hortonworks, watch this webinar to discover how to unify all your data in Hadoop, without specific skills Big Data.
Hadoop ist nicht mehr länger nur eine Option. Unternehmen aller Größenordnungen befinden sich an den verschiedensten Stationen auf ihrer Big-Data-Reise. Ganz gleich, ob Sie gerade erst beginnen die Plattform zu erkunden oder ob Sie bereits mehrere Cluster im Betrieb haben. Jeder steht der gleichen Herausforderung gegenüber: Aufbau interner Kompetenzen.
Hadoop-Spezialisten sind schwer zu finden. Von Hand zu programmieren ist zu fehlerträchtig, wenn es an das Storing, die Integration oder die Analyse Ihrer Daten geht. Es geht jedoch auch einfacher.
Begleiten Sie Talend und Hortonworks bei diesem Webinar in welchem wir Ihnen aufzeigen, wie Sie Ihre Daten in Hadoop vereinen können - und zwar ohne spezielle Big-Data-Kenntnisse.
Anhand einer technischen Demo werden Sie erfahren, wie:
· Hadoop völlig neue Analyse-Applikationen ermöglicht
· Sie die Kompetenz-Kluft mit unseren Big-Data-Lösungen überbrücken können
A strong data governance program ensures that you have the policies, standards, and controls in place to protect data effectively and access it for decision making. Data governance may become one of the most important functions of your data integration architecture when it comes to data agility.Read more >
GDPR has major implications for the IT landscape of every business. Companies are shifting away from data silos and closing down shadow IT to create a governed personal data hub and data services.
In this webinar we demonstrate how to achieve the 5 pillars of data management for GDPR compliance with a practical example: matching 3rd party consent data to existing records with appropriate data governance.
See how easily you can put the pieces together:
Data capture and integration into a data lake
Data cleansing, loading and matching
Self-service curation and certification
Data classification and lineage using metadata manager
The world’s leading firms have recognized that data, along with human capital, is the most valuable asset they have today. The need for IT to digitize the business and provide actionable data to forecast market movements, improve customer experience, make flash offers, response to network errors, is paramount to sustainability. However, existing systems were not designed to address the data needs of today’s enterprises. The conclusion - organisations are turning to Apache Hadoop to become Data-Driven. Join this webinar to learn:
The importance of becoming Data-Driven
How-to build a Modern Data Architecture with Open Enterprise Hadoop
How-to use Hadoop to build a real-time infrastructure
Check-list for success
Process Big Data with Spark on Microsoft Azure HDInsight
Machine learning helps pinpoint errors in large datasets for cleansing before entering the analytics pipeline. This on-demand webinar shows you how to set it up.
Big data brings tremendous opportunity to better target customers and improve operations. Yet, data-driven insights are only as good and trusted as the data going into them.
Find out how you can build data quality into your structured, semi-structured, or unstructured data on Microsoft Azure Data Lake Store and HDInsight using Talend’s native support for Spark machine learning algorithms.
Watch Microsoft and Talend to see how to:
- Process data faster using Talend’s native support for Spark On Azure HDInsight
- Quickly import bulk data into Azure Data Lake Store
- Deploy Spark machine learning to match and dedupe records at scale
Enable best practices for data quality using Talend Data Stewardship
With ever increasing volumes and varieties of data forming the basis of our analysis universe, Machine Learning promises to help automate and condense cycle time to actionable insight. But how can you overcome the stubborn complexities of adoption associated with this subset of Data Science?
In this on-demand webinar see how Talend helps its data-driven customers embrace and exploit Machine Learning capabilities faster and more efficiently than traditional, coding-based approaches.
In this on-demand webinar you will learn about:
- The challenges of generating insight from data
- Principles of machine learning
- How to train, test and operationalise Machine Learning solutions using Talend
- How other Talend customers are exploiting Big Data solutions
Nello Franco describes why customer success should be a company-wide philosophy that every client-facing aspect of your organization should be accountable for.Read more >
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 50% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming analytics ingestion, and data wrangling within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Spark, Talend or KNIME. The session also discusses how this is related to visual analytics, and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various option for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation