Our Training Courses
We offer variety of training courses on our area of expertise, BigData and Cloud technologies
The courses were created by leading industry experts based on their vast experience - and they are the actual instructors too
All our courses are delivered in small classes, include many hands-on exercises and are suitable for experienced developers
Elasticsearch is today's de-facto standard for centralized logging and real-time analytics for system metrics and business data. What started as a full-text search engine quickly became much, much more than just that.
This intensive 2-day hands-on workshop is designed to teach you the Elastic Stack from the ground up. You will start by learning the basics of full-text search and information retrieval and the important Elasticsearch APIs: Document, Indexes, Queries and Aggregations APIs.
Only once you are fluent with the Elasticsearch APIs you can really use the powerful Elastic Stack to its full potential. Formerly known as ELK, the Elastic Stack has several components (Elasticsearch, Logstash, Kibana, Beats, and several Stack-wide solutions), and you will learn how to make good use of them all.
This 1-day course is aimed at developers and operations people who need to be able to maintain Elasticsearch clusters in production. In this course you will learn about the various parts that make up a cluster, how it operates, and many do's and don'ts learned by experience over the years.
Apache Spark is the main platform for deep BigData analysis. Companies from all industries - Finance, AdTech, Cyber, Commerce and Internet are using Spark in different modes for ETL, BI, Machine-learning and stream-processing. This developers course gives you hands-on experience with Spark basic and advanced modules, and is focused on Spark DataFrames - the data-optimized API of Spark. Teaching and exercises are done on a cloud environment (AWS EMR, S3 and Zeppelin).
What do cancer detection, sentiment analysis, image recognition, machine translation and playing atari games have in common? These are all complex real-world tasks, and the goal of artificial intelligence (AI) is to tackle these with powerful mathematical and programmatic tools. In this course, you will learn the foundational principles that enables machines to make autonomous decisions and practice implementing some of these systems. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in your field of interest.
This 16 hour instructor-led class introduces participants to the Big Data & Machine Learning capabilities of Google Cloud Platform. It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.
How can we store or process large volumes of data? how to deal with a massive stream of events coming at high velocity? what really is BigData? How does a BigData-ready system look like? and how can Clouds help?
The topics of BigData and Cloud technologoies are being mentioned a lot, but it's hard to know where to start or what technologies to use.
Join our internationally renowned instructors for a full day packed of knowledge sharing. Let us give you an overview with short deep-dives into the vast landscapes of BigData, everything you need to get started with the technologies that changed the world.
Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
This 1-day training course teaches how to design and implement a graph data model and associated queries. With a mixture of instruction and hands-on practice sessions, you’ll learn how to apply the property graph model to solve common modeling problems. You’ll also learn how to evolve an existing graph in a controlled manner to support new or changed requirements.