Big Data Training
Turn your sea of data into a competitive edge with Big Data training.
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Expert practitioners teach your employees how to turn today's data into tomorrow's profit.
Instructor-Led | Role-Based | Tailored To Your Project
The quantity of data coming into your organization has grown exponentially in recent years. It’s coming faster, from more sources and in more formats. Your challenge? To harness this data, clean it and make it usable for decision-making.
Through innovative Big Data training courses, we teach your employees how to convert data into actionable insights. Development, analytics, intelligence—we cover it all.
Our Big Data Training Team
Our Big Data training instructors are experts who have worked at various U.S. federal government agencies and companies like Zappos. One even had a startup acquired by Oracle. When they aren’t training for DevelopIntelligence, they sling code and author blog posts on the technologies they use. They also speak at industry conferences and contribute to open-source projects.
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Big Data training with DevelopIntelligence prepares your teams to excel.
Available Courses:
Learn about and compare four different data visualization tools.
Learn about Data Warehousing from an AWS perspective examining tools specifically underneath AWS.
Learn how to use the high-throughput, distributed, publish-subscribe messaging system Apache Kafka.
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 work with Apache Lucene.
Learn about the latest and greatest features of Lucene.
Learn how to configure and work with Apache Solr.
Learn how to use Spark internals for working with NoSQL databases as well debugging and troubleshooting.
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 Architects
This course introduces you to everything a cloud solutions architect needs to know to plan and design solutions for Microsoft Azure cloud platform.
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.
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.
Learn to use Google's BigQuery to explore and gain insights from large datasets.
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.
Big Data Integration with Talend
Learn how to use Talend through hands-on labs and real-world projects that students may encounter while building big data applications.
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 Engineering: dbt + Snowflake
Learn how to use dbt and Snowflake to transform data more effectively.
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 and Grafana
Learn to use Prometheus for monitoring and alerting and Grafana for data visualization.
Data Analysis & Visualizations with Excel
Learn how to use Excel for data analysis to present data into visual insights.
Learn an in-depth understanding of Microsoft Power BI.
Learn to use Gemfire in high performance systems in order to facilitate fast access to data.
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).
Learn how to use Grafana to connect with, visualize and alert on various types of data.
Working with Prometheus and Grafana
Learn to use Prometheus for monitoring and alerting and Grafana for data visualization.
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.
Learn about and compare four different data visualization tools.
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.
Learn how to use MongoDB with Python.
Learn about DynamoDB and its benefits.
Learn an in-depth understanding of Microsoft Power BI.
Working with Prometheus and Grafana
Learn to use Prometheus for monitoring and alerting and Grafana for data visualization.
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.
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.
Learn how to integrate Redis into an application and/or stack.
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 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.
Learn how to work with Apache Lucene.
Learn how to configure and work with Apache Solr.
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.
Learn about the latest and greatest features of Lucene.
Learn how to work with ElasticSearch.
Learn how to use the ELK stack.
Data Engineering: dbt + Snowflake
Learn how to use dbt and Snowflake to transform data more effectively.
Learn how to utilize and manage Snowflake databases.
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.
Big Data Integration with Talend
Learn how to use Talend through hands-on labs and real-world projects that students may encounter while building big data applications.
