Big Data Training
DevelopIntelligence specializes in delivering highly-customized, dedicated, role-based Big Data Training courses to technical teams and organizations.
Instructor-led Big Data Courses
Customized, role-based, expert-led Big Data Training
Big Data is defined, by the Gartner Group, as information that includes the following four dimensions: velocity, variety, volume and veracity. Nearly every industry will be affected (or in some cases revolutionized) by Big Data.
Our big data training instructors are experts who have worked at different U.S. federal government agencies, had their startup acquired by Oracle, and worked at companies like Zappos. When they aren’t training for us, they regularly are slinging coding, authoring blog posts on the tech they use, speaking at industry conferences, and contributing to open source projects.
Looking to dive into a Big Data course? Check out our Big Data Fast Track and Applying Big Data Technologies course or let us know if we can work with your team on a perfect, custom cloud computing training course.
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Introduction to Apache Flink -
Learn about scalable batch and stream data processing using Apache Flink.
Introduction to Apache Storm -
Learn about Storm, the real time processing framework for Hadoop.
Introduction to Apache Zeppelin -
Learn about the interactive data analytics User Interface (UI) framework, Apache Zeppelin, that allows ease of access to data coming from several big data implementations.
Introduction to Apache ZooKeeper -
Learn about the internals of Zookeeper and explores how it functions.
Introduction to ETL Management with Airflow -
Learn how to use Apache Airflow to manage data warehouses.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
Introduction to Apache Spark -
Learn how to use Apache Spark as an alternative to traditional MapReduce processing.
Introduction to Apache Spark in Production -
Learn about the architecture and internals of Spark, a fast and general engine for big data processing with built-in modules for streaming, SQL, machine learning, and graph processing.
Learn about and build end-to-end SML pipelines for gaining actionable insights.
Learn best practices and techniques to optimize Spark Core and Spark SQL code.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn how to deploy, configure and manage Azure using Chef.
Azure for System Administrators -
Learn how to configure, maintain and monitor Azure Virtual Machines and Virtual Networks.
Learn how to build, deploy, and maintain applications in Azure Cloud.
Developing Scalable Applications on Azure -
Learn how to develop a globally distributed application using Azure and serverless concepts
Implementing Microsoft Azure Infrastructure Solutions Certification -
Learn how to pass the "Implementing Microsoft Azure Infrastructure Solutions" certification exam.
Introduction to Artificial Intelligence and Machine Learning in Azure -
Learn how to implement AI and ML techniques on Azure.
Introduction to Azure for Developers -
Learn how to build, deploy, secure, scale, monitor and maintain applications in Azure Cloud.
Introduction to Azure for Managers -
Learn how to plan and manage cloud team resources more effectively.
Introduction to the Azure Stack and VM Managers -
Learn how to deploy and manage Azure in private data centers.
Microsoft Azure Certification Boot Camp -
Learn the information necessary to pass the Developing Microsoft Azure Solutions Certification Exam.
Working with Big Data on Azure -
Learn how to use Big Data technologies on the Azure cloud.
Learn about the functions of Cassandra required to build a Cassandra-based application.
Introduction to Cassandra Learning Spike -
Learn how to develop with Cassandra.
Learn to build end-to-end data applications using Microsoft Azure and understand which tools are best suited to certain problems and use-cases.
Learn how to locate, manipulate, and analyse data with Python, no matter the size of the data set.
Data Architecture Fundamentals -
Learn techniques and tools for data collection, usage, processing, storage, and integration with different systems.
Learn the foundational concepts of distributed computing, distributed data processing, data management and data pipelines.
Data Integration with Talend -
Learn how to use Talend to make data more accessible, improve its quality, and help your organization become more data-driven.
Learn about the strengths, weaknesses, opportunities and risks surrounding data-based solutions.
Learn how to leverage R to perform data analytics.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
Learn the basic concepts of data engineering and how to make the shift to the cloud.
Data Analysis & Visualizations with Excel -
Learn how to use Excel for data analysis to present data into visual insights.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn about and compare four different data visualization tools.
Learn how to evaluate a visuals request, choose the most appropriate visualization tool, and create effective visuals to suit the needs of the request.
Learn an in-depth understanding of Microsoft Power BI.
Working with Prometheus & Grafana -
Learn to use Prometheus for monitoring and alerting and Grafana for data visualization.
Learn to use Gemfire in high performance systems in order to facilitate fast access to data.
Learn how to create, aggregate, and monitor microservices in Go.
Teach developers how to use the Go language.
Building Chatbots Using Google Dialogflow -
Learn how to build a Chatbot on Google Cloud and deploy it standalone as well as on Facebook Messenger.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Google Cloud for Data Analysts -
Learn how to perform insightful and responsive data analysis at scale and delight the consumers of the analysis with effective data visualizations.
Google Cloud for Data Engineers -
Learn to build systems on Google Cloud to store and process batch or streaming data.
Google Cloud for Data Scientists -
Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.
Learn to create and deploy software on Google Cloud to have secure and stable applications.
Learn about Google Cloud and its technical capabilities in the areas of IT infrastructure, operations, Big Data, and Machine Learning.
Intermediate Google Cloud For Data Analysts -
Learn how to analyze large scale, distributed, and real-time datasets with MapReduce and Apache Beam based capabilities of Google Cloud and practice identification and analysis of effective data features for predictive analytics with BigQuery ML and TensorFlow.
Intermediate Google Cloud for Data Engineers -
Learn how to use data engineering on Google Cloud to build specialized data pipelines for large scale streaming data analytics, machine learning, and recommendation systems.
Intermediate Google Cloud For Data Scientists -
Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students’ understanding of underlying mathematical principles.
Intermediate Google Cloud for Developers -
Learn how to utilize advanced processes and practices for the platform to improve function and security.
Learn to use Google's BigQuery to explore and gain insights from large datasets.
Introduction to Cloud Computing -
Learn about the different cloud providers and what they offer.
Introduction to the Google App Engine -
Learn how to use Java to set up a project with the Google App Engine and integrate it with other Google Cloud services.
Moving to a Hybrid Cloud for Managers Adding Google Cloud -
Learn how to develop a platform-based infrastructure using Google Cloud.
Learn the fundamental skills needed to manage large scale infrastructure with SaltStack on major cloud providers (AWS, Google Cloud and Azure).
Applying Big Data Technologies -
Learn to use big data technologies and understand their tradeoffs.
Learn all about Hadoop and Big Data technologies.
Learn how to maintain and operate a Hadoop cluster.
Learn the fundamentals of the Hadoop platform.
Learn how to use Hadoop to manage, manipulate, and query large complex data in real time.
Learn how to implement secure Hadoop clusters using authentication, authorization, and encryption.
Introduction to Administering Hadoop Clusters -
Learn how to set, configure, and administer Hadoop.
Introduction to Hadoop Administration -
Learn how to administer and maintain Hadoop.
Introduction to Hadoop for Developers -
Learn how to write MapReduce programs using Java.
Introduction to Hadoop for Managers -
Learn how Hadoop fits into organization infrastructures.
Introduction to Apache Kafka -
Learn how to use the high-throughput, distributed, publish-subscribe messaging system Apache Kafka.
Introduction to Kafka Streams -
Learn to utilize Kafka Streams and identify where Kafka can be further incorporated into practice.
Learn to use Apache Kafka as a distributed messaging system.
Real-Time Ingestion & Processing Using Kafka & Spark -
Learn the knowledge and skills needed to utilize Data Ingestion and Processing using Kafka and Spark Streaming.
Learn how to use all parts of the MEAN stack together to create functional full-stack applications.
MongoDB for Python Developers -
Learn how to use MongoDB with Python.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
Fundamentals of Apache CouchDB -
Learn the fundamentals of Apache CouchDB.
Learn about the functions of Cassandra required to build a Cassandra-based application.
Introduction to Cassandra Learning Spike -
Learn how to develop with Cassandra.
Learn to use the NoSQL database Redis.
Learn how to use all parts of the MEAN stack together to create functional full-stack applications.
MongoDB for Python Developers -
Learn how to use MongoDB with Python.
Learn about DynamoDB and its benefits.
Learn how to integrate Redis into an application and/or stack.
Learn advanced techniques for managing data and tuning models.
Learn how to become an expert at creating high-throughput, multithreaded, network oriented programs written in Python.
Applied Data Science & Machine Learning -
Learn intermediate data analysis techniques and how programming languages can be used to further analyze data.
Building a Recommendation Engine Using Python -
Learn to design, implement and evaluate various recommendation engines.
Learn how to mature programming knowledge.
Introduction to Data Science & Machine Learning -
Learn how data can be gathered to improve the overall needs of the business.
Introduction to Graphics Processing -
Learn a working knowledge of graphics processing practices within Machine Learning using CUDA, PyCuda, OpenCL, Vulkan, and Tensorflow.
Introduction to Machine Learning -
Learn various Machine Learning algorithms to evaluate and productize models.
Learn how to create basic programs using Python.
Learn how to develop simple programs using fundamental Python concepts.
Learn both R and Python programming languages by providing comparisons and recommendations between both.
MongoDB for Python Developers -
Learn how to use MongoDB with Python.
Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.
Learn how to implement statistical and machine learning models using PyTorch and how to improve their performance based on an understanding of underlying mathematical principles.
Software Engineering in Python -
Learn software engineering techniques using Python.
Learn how to use R as a tool to perform data science, machine learning or statistics on large data sets.
Engineering Reactive Architecture Using Scala, Akka, Play -
Learn how to use Reactive Programming with Scala as a foundation.
Learn how to quickly build web applications in Scala using the Akka framework.
Introduction to Akka with Java -
Learn how to use the Akka Framework with Java to build distributed applications.
Introduction to Play Framework -
Learn how to quickly build web applications in Scala using the Play framework.
Learn how to adopt Scala to efficiently build multi-core processing applications.
Introduction to Scala Learning Spike -
Learn the fundamentals of the Scala programming language.
Scala Using the Typelevel Stack -
Learn the Typelevel stack and type class fundamentals.
Test-Driven Development with Scala -
Learn how to effectively test Scala based applications.
Learn how to use modern Solr and SolrCloud.
Introduction to Apache Lucene -
Learn how to work with Apache Lucene.
Learn how to configure and work with Apache Solr.
Introduction to Elasticsearch -
Learn the key concepts required to adopt Elasticsearch.
Learn the basics of search within the Solr/Lucene context.
Learn the knowledge and use cases for software engineers to transition to Machine Learning for Search.
Migrating to Apache Lucene 5 -
Learn about the latest and greatest features of Lucene.
Learn how to work with ElasticSearch.
Learn how to use the ELK stack.
Learn how to utilize and manage Snowflake databases.
Learn how to configure and work with Apache Solr.
Learn how to improve analytical ability and the ability to provide data insights.
Learn how to identify the right context for analysis, perform the analysis and tell a story to drive action.
Learn advanced Tableau knowledge and skills.
Learn the fundamentals of Tableau.
