Big Data Training in Erfurt

Participants who complete this instructor-led, live training in Erfurt will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

This instructor-led, live training in Erfurt (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications. By the end of this training, participants will be able to:

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in Erfurt (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows. By the end of this training, participants will be able to:

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for data migration within their organizations. By the end of this training, participants will be able to:

• Understand the foundational concepts and components of various data architectures.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
• Implement and manage data governance frameworks such as Dama and Togaf.
• Leverage cloud platforms for efficient data storage, processing, and management.

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions. By the end of this training, participants will be able to:

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level IT professionals who wish to have a comprehensive understanding of IBM DataStage from both an administrative and a development perspective, allowing them to manage and utilize this tool effectively in their respective workplaces. By the end of this training, participants will be able to:

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level big data professionals who wish to utilize Apache Kylin for building real-time data warehouses and performing multidimensional analysis on large-scale datasets.By the end of this training, participants will be able to:

• Set up and configure Apache Kylin with real-time streaming data sources.
• Design and build OLAP cubes for both batch and streaming data.
• Perform complex queries with sub-second latency using Kylin's SQL interface.
• Integrate Kylin with BI tools for interactive data visualization.
• Optimize performance and manage resources effectively in Kylin.

Participants who complete this instructor-led, live training in Erfurt will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

In this instructor-led, live training in Erfurt, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises. By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level database administrators, developers, and analysts who wish to master advanced SQL functionalities for complex data operations and database management. By the end of this training, participants will be able to:

• Perform advanced querying techniques using unions, subqueries, and complex joins.
• Add, update, and delete data, tables, views, and indexes with precision.
• Ensure data integrity through transactions and manipulate database structures.
• Create and manage databases efficiently for robust data storage and retrieval.

Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries. In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories. By the end of this training, participants will be able to:

• Install and configure Dremio
• Execute queries against multiple data sources, regardless of location, size, or structure
• Integrate Dremio with BI and data sources such as Tableau and Elasticsearch

Audience

• Data scientists
• Business analysts
• Data engineers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Notes

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Erfurt (online or onsite) is aimed at intermediate-level database professionals who wish to enhance their skills in Oracle SQL development and administration. By the end of this training, participants will be able to:

• Build and optimize complex SQL queries.
• Manage databases efficiently using Oracle SQL tools.
• Apply best practices in database development and maintenance.
• Administer user access and database security in an Oracle environment.

Apache Accumulo is a sorted, distributed key/value store that provides robust, scalable data storage and retrieval. It is based on the design of Google's BigTable and is powered by Apache Hadoop, Apache Zookeeper, and Apache Thrift. This instructor-led, live courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo. Format of the Course

• Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding

This instructor-led, live training in Erfurt (online or onsite) is aimed at application developers and engineers who wish to master more sophisticated usages of the Teradata database. By the end of this training, participants will be able to:

• Manage Teradata space.
• Protect and distribute data in Teradata.
• Read Explain Plan.
• Improve SQL proficiency.
• Use main utilities of Teradata.

This course is intended for developers and data scientists who want to understand and implement artificial intelligence in their applications. Special focus is placed on data analytics, distributed AI, and natural language processing.

Amazon Redshift is a petabyte-scale cloud-based data warehouse service in AWS. In this instructor-led, live training, participants will learn the fundamentals of Amazon Redshift. By the end of this training, participants will be able to:

• Install and configure Amazon Redshift
• Load, configure, deploy, query, and visualize data with Amazon Redshift

Audience

• Developers
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

Apache ActiveMQ is an open source message broker written in Java.

Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information. High-value government solutions will be created from a mashup of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured. But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog. The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it. The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge. Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.). Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.

Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam's supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system. In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing. By the end of this training, participants will be able to:

• Install and configure Apache Beam.
• Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.
• Execute pipelines across multiple environments.

Format of the Course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• This course will be available Scala in the future. Please contact us to arrange.

This classroom based training session will explore Big Data. Delegates will have computer based examples and case study exercises to undertake with relevant big data tools

Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise. Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions. Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.  Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.  Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4. 

In this instructor-led, live training in Erfurt, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.By the end of this training, participants will be able to:

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.

When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons. This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.

The course is part of the Data Scientist skill set (Domain: Data and Technology).

This instructor-led, live training (online or onsite) is aimed at engineers who wish to use Confluent (a distribution of Kafka) to build and manage a real-time data processing platform for their applications. By the end of this training, participants will be able to:

• Install and configure Confluent Platform.
• Use Confluent's management tools and services to run Kafka more easily.
• Store and process incoming stream data.
• Optimize and manage Kafka clusters.
• Secure data streams.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• This course is based on the open source version of Confluent: Confluent Open Source.
• To request a customized training for this course, please contact us to arrange.

Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run, so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

In this instructor-led, live training in Erfurt, participants will learn how to build a Data Vault. By the end of this training, participants will be able to:

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

This instructor-led, live training in Erfurt (online or onsite) is aimed at architects, developers, and administrators who wish to use Denodo Platform to optimize and accelerate data management through data virtualization. By the end of this training, participants will be able to:

• Install and configure Denodo Platform.
• Understand the features and architecture of Denodo Platform.
• Understand the key concepts, benefits, and use cases for data virtualization.
• Learn how to configure and manage the Denodo Platform server.
• Implement data security, user access, and services authentication.
• Apply the tools and techniques for operations monitoring and performance optimization.

Apache Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this instructor-led, live course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

This instructor-led, live training in Erfurt (online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink. By the end of this training, participants will be able to:

• Set up an environment for developing data analysis applications.
• Understand how Apache Flink's graph-processing library (Gelly) works.
• Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.
• Manage diverse workloads.
• Perform advanced analytics.
• Set up a multi-node Flink cluster.
• Measure and optimize performance.
• Integrate Flink with different Big Data systems.
• Compare Flink capabilities with those of other big data processing frameworks.

In this instructor-led, live training in Erfurt, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to:

• Understand how graph data is persisted and traversed.
• Select the best framework for a given task (from graph databases to batch processing frameworks.)
• Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
• View real-world big data problems in terms of graphs, processes and traversals.

This instructor-led, live training in Erfurt (online or onsite) is aimed at administrators who wish to set up Greenplum Database for business intelligence and data warehousing solutions. By the end of this training, participants will be able to:

• Address processing needs with Greenplum.
• Perform ETL operations for data processing.
• Leverage existing query processing infrastructures.

This instructor-led, live training in Erfurt (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution. By the end of this training, participants will be able to:

• Use Hortonworks to reliably run Hadoop at a large scale.
• Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
• Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
• Process different types of data, including structured, unstructured, in-motion, and at-rest.

Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters. Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation. Audience This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools. After this course delegates will be able to

• Extract meaningful information from Hadoop clusters with Impala.
• Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
• Troubleshoot Impala.

In this instructor-led, live training in Erfurt (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices. By the end of this training, participants will be able to:

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This instructor-led, live training in Erfurt (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python. By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.

Kafka Streams is a client-side library for building applications and microservices whose data is passed to and from a Kafka messaging system. Traditionally, Apache Kafka has relied on Apache Spark or Apache Storm to process data between message producers and consumers. By calling the Kafka Streams API from within an application, data can be processed directly within Kafka, bypassing the need for sending the data to a separate cluster for processing. In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing. By the end of this training, participants will be able to:

• Understand Kafka Streams features and advantages over other stream processing frameworks
• Process stream data directly within a Kafka cluster
• Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
• Write concise code that transforms input Kafka topics into output Kafka topics
• Build, package and deploy the application

Audience

• Developers

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Notes

• To request a customized training for this course, please contact us to arrange

This instructor-led, live training in Erfurt (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code. By the end of this training, participants will be able to:

• Install and configure Confluent KSQL.
• Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
• Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
• Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.

This instructor-led, live training in Erfurt (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data. By the end of this training, participants will:

• Understand the evolution and trends for machine learning.
• Know how machine learning is being used across different industries.
• Become familiar with the tools, skills and services available to implement machine learning within an organization.
• Understand how machine learning can be used to enhance data mining and analysis.
• Learn what a data middle backend is, and how it is being used by businesses.
• Understand the role that big data and intelligent applications are playing across industries.

In this instructor-led, live training in Erfurt (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment. By the end of this training, participants will be able to:

• Install and configure Apachi NiFi.
• Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
• Automate dataflows.
• Enable streaming analytics.
• Apply various approaches for data ingestion.
• Transform Big Data and into business insights.

In this instructor-led, live training in Erfurt, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to:

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

Apache SolrCloud is a distributed data processing engine that facilitates the searching and indexing of files on a distributed network. In this instructor-led, live training, participants will learn how to set up a SolrCloud instance on Amazon AWS. By the end of this training, participants will be able to:

• Understand SolCloud's features and how they compare to those of conventional master-slave clusters
• Configure a SolCloud centralized cluster
• Automate processes such as communicating with shards, adding documents to the shards, etc.
• Use Zookeeper in conjunction with SolrCloud to further automate processes
• Use the interface to manage error reporting
• Load balance a SolrCloud installation
• Configure SolrCloud for continuous processing and fail-over

Audience

• Solr Developers
• Project Managers
• System Administrators
• Search Analysts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Erfurt (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data. By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.

MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs. It divides into two packages:

• spark.mllib contains the original API built on top of RDDs.
• spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark

This instructor-led, live training in Erfurt (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data. By the end of this training, participants will be able to:

• Install and configure Talend Open Studio for Big Data.
• Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
• Understand and set up Open Studio's big data components and connectors.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to run Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.

Teradata is one of the popular Relational Database Management System. It is mainly suitable for building large scale data warehousing applications. Teradata achieves this by the concept of parallelism.  This course introduces the delegates to Teradata.

This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment. By the end of this training, participants will be able to:

• Install and configure Zeppelin
• Develop, organize, execute and share data in a browser-based interface
• Visualize results without referring to the command line or cluster details
• Execute and collaborate on long workflows
• Work with any of a number of plug-in language/data-processing-backends, such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown and Shell.
• Integrate Zeppelin with Spark, Flink and Map Reduce
• Secure multi-user instances of Zeppelin with Apache Shiro

ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.