Everything Data Scientists Should Know About Organizing Data Lakes

simplilearn.com

The recent GDPR regulations have changed the way companies handle the data they collect and store, making it imperative for data scientists to explore innovative ways to crunch and catalog data while ensuring that the company adheres to these new rulings that seek to establish complete data security.Join Big Data Expert Ronald Van Loon, and Simplilearns Chief Product Officer Anand Narayanan in a live video chat to understand.
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Spotlight

AT A GLANCE. Traditional SIEM detection methodologies are essential, however the limitation is based on the dependence of logs which are generated through traditional security controls. Our Network Behavioural Analytics (or NBA) service detects patterns and anomalies beyond those which IPS and firewalls can detect. We tap into network traffic to highlight behavioural anomalies based on packet communications by using applied mathematics and data science. HOW IT WORKS. We use Darktrace unsupervised machine learning to analyse network data at scale, and make billions of probability-based calculations which draw from the evidence that it identifies. Instead of relying on knowledge of past threats, it independently classifies data and detects compelling patterns.

OTHER ON-DEMAND WEBINARS

SMALL DATA, BIG INSIGHTS

Agillic

There is a lot of focus on big data, but sometimes it makes sense to look at the small data at hand and explore how to drive value from that. In this webinar, Mike Weston will present how to iteratively work with the data available, from e.g. purchase patterns, web browsing or location data, and gradually grow big insights.
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Approaching Data Management Technologies

infogix

Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of Data Management technologies that can be used to increase the productivity of Data Management efforts.
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Using MySQL for Distributed Database Architectures

Percona

In modern data architectures, we’re increasingly moving from single node design systems to distributed architectures using multiple nodes often spread across multiple databases and multiple continents. Such architectures bring many benefits (such as scalability and resiliency), but can also bring a lot of pain if incorrectly architected.
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How to Modernize Data Lake Technologies with Cloud-Based Solutions

tdwi.org

Data lakes based on Hadoop technologies have proved themselves valuable in mission-critical use cases such as data warehousing, advanced analytics, multichannel marketing, complete customer views, digital supply chains, and the modernization of data management.Most Hadoop users are committed to the data lake method of managing data, but they are limited by Hadoop shortcomings in key areas such as cluster maintenance, administration cost, resource management, metadata management, and support for SQL and other relational technologies. Many view cloud-based solutions as the optimal replacement for their data lake, but they are not ready to make such a significant change. The truth is: they don't have to, as the two technologies can coexist.
Watch Now

Spotlight

AT A GLANCE. Traditional SIEM detection methodologies are essential, however the limitation is based on the dependence of logs which are generated through traditional security controls. Our Network Behavioural Analytics (or NBA) service detects patterns and anomalies beyond those which IPS and firewalls can detect. We tap into network traffic to highlight behavioural anomalies based on packet communications by using applied mathematics and data science. HOW IT WORKS. We use Darktrace unsupervised machine learning to analyse network data at scale, and make billions of probability-based calculations which draw from the evidence that it identifies. Instead of relying on knowledge of past threats, it independently classifies data and detects compelling patterns.

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