NoSQL Training Courses

NoSQL Training

Not Only SQL Databases, not relational databases courses

Testi...Client Testimonials

MongoDB for Administrators

monitoring

Ling Xiao - The Globe and Mail

MongoDB for Administrators

Good content and exercises

Richard Smallwood - PayPoint Network Limited

MongoDB for Developers

open mind and communication

Oleksiy Deliyev - Insight Enterprises

MongoDB for Developers

super athmosphere, working with mongo shell

Jan Sturm - AVL List GmbH

MongoDB for Administrators

The structure and pace of the class was great.

David Lacy - Availity

MongoDB for Administrators

The depth of the Mongo db training was explored from basic to advanced, I felt it was a little too much to squeeze into 2 days but I did get exposure to all aspects of Mongo db.

Bay Sayarath - Availity

MongoDB for Administrators

Relevant to need.

Damon Grube - Availity

MongoDB for Administrators

Most of the hands on stuff was good.

Andrew Bauer - Availity

MongoDB for Administrators

I had attended a different training given by the mongo team. I like this one a lot better in terms of simplicity and course material. Thanks for helping us out.

Patience, clear and to the point.

V. Rai - New Jersey

MongoDB for Developers

He (the trainer) used good real world examples and pitched the exercises at the right level

Martin Davies- Capgemini UK Plc

MongoDB for Administrators

tailored to cover our organisations questions.

Robin Bell - Egress Software Technologies

MongoDB for Administrators

The clear depth of knowledge the trainer had, which really shone when combined with his evident enthusiasm for the subject.

Joseph Brailsford - Egress Software Technologies

MongoDB for Administrators

Even though I have been using MongoDB for a while, there were still some new "basic" things that Kamil taught us - as well as teaching us the advanced topics we need to move our projects forwards.

Adam McKay - Egress Software Technologies

A practical introduction to Data Analysis and Big Data

Willingness to share more

Balaram Chandra Paul - MOL Information Technology Asia Limited

NoSQL Course Outlines

Code Name Duration Overview
bigddbsysfun Big Data & Database Systems Fundamentals 14 hours The course is part of the Data Scientist skill set (Domain: Data and Technology). Data Warehousing Concepts What is Data Ware House? Difference between OLTP and Data Ware Housing Data Acquisition Data Extraction Data Transformation. Data Loading Data Marts Dependent vs Independent data Mart Data Base design ETL Testing Concepts: Introduction. Software development life cycle. Testing methodologies. ETL Testing Work Flow Process. ETL Testing Responsibilities in Data stage.       Big data Fundamentals Big Data and its role in the corporate world The phases of development of a Big Data strategy within a corporation Explain the rationale underlying a holistic approach to Big Data Components needed in a Big Data Platform Big data storage solution Limits of Traditional Technologies Overview of database types NoSQL Databases Hadoop Map Reduce Apache Spark
riak Riak: Build Applications with High Data Accuracy 14 hours Riak is an Erlang based open-source document database, similar to CouchDB. It is created and maintained by Basho. In this instructor-led, live training, participants will learn how to build, run and operate a Riak based web application. By the end of this training, participants will be able to: Extend the number of hardware nodes and partition data across multiple servers Use bucket/key/values to organize and retrieve documents Use full-text search like query syntax Understand other Riak related technologies, such as Riak KV and Riak TS Test, secure, optimize and deploy a sample web application Audience Developers Database engineers Operations staff Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us.
BigData_ A practical introduction to Data Analysis and Big Data 35 hours Participants who complete this training 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. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress. Introduction to Data Analysis and Big Data What makes Big Data "big"? Velocity, Volume, Variety, Veracity (VVVV) Limits to traditional Data Processing Distributed Processing Statistical Analysis Types of Machine Learning Analysis Data Visualization Languages used for Data Analysis R language Why R for Data Analysis? Data manipulation, calculation and graphical display Python Why Python for Data Analysis? Manipulating, processing, cleaning, and crunching data Approaches to Data Analysis Statistical Analysis Time Series analysis Forecasting with Correlation and Regression models Inferential Statistics (estimating) Descriptive Statistics in Big Data sets (e.g. calculating mean) Machine Learning Supervised vs unsupervised learning Classification and clustering Estimating cost of specific methods Filtering Natural Language Processing Processing text Understaing meaning of the text Automatic text generation Sentiment analysis / Topic analysis Computer Vision Acquiring, processing, analyzing, and understanding images Reconstructing, interpreting and understanding 3D scenes Using image data to make decisions Big Data infrastructure Data Storage Relational databases (SQL) MySQL Postgres Oracle Non-relational databases (NoSQL) Cassandra MongoDB Neo4js Understanding the nuances Hierarchical databases Object-oriented databases Document-oriented databases Graph-oriented databases Other Distributed Processing Hadoop HDFS as a distributed filesystem MapReduce for distributed processing Spark All-in-one in-memory cluster computing framework for large-scale data processing Structured streaming Spark SQL Machine Learning libraries: MLlib Graph processing with GraphX Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing the right solution for the problem The future of Big Data Closing remarks
mongodbadmin MongoDB for Administrators 14 hours This course covers everything a database administrator needs to know to successfully deploy and maintain MongoDB databases. Diagnosing performance issues, importing and exporting data, and establishing the proper backup and restore routines, overview of the MongoDB CRUD API, the command shell, and the drivers. are also covered. The audience of this course include people who want to: Understand MongoDB from a developer's perspective, including its command shell, query API, and driver tools. Deploy MongoDB in all its configurations - as a single server, with master/slave replication, as a replica set, and as a sharded cluster. Evaluate applications and choose hardware appropriately. Monitor MongoDB instances and integrate with standard monitoring software (Munin, Nagios, etc.) Plan for backups and manage large data imports and exports. Troubleshoot the most common developer issues and failure scenarios. Each delegate will need to perform a series of practical exercises. MongoDB Architectural Overview Origin, design goals, key features Process structure (mongos, mongod, config servers) Directory / file structure Working with the MongoDB Shell Documents and data types CRUD (Inserts, queries, updates, deletes) System commands Single-server Configuration and Deployment Configuration files Data files and allocation Log files Hardware and file-system recommendations Security Built-in authentication Recommendations for secure deployment Monitoring MongoDB mongostat Analyzing memory and IO performance Integration with monitoring tools: Munin / Cacti / Nagios MongoDB's web console Indexing and Query Optimization Managing indexes and MongoDB indexing internals Single / Compound / Geo indexes Identifying sub-optimal queries. Using the query profiler. Introduction to drivers (Java/Python/Ruby/PHP/Perl) How the drivers and shell communicate with MongoDB BSON and the MongoDB Wire Protocol Troubleshooting application connections Intro to Read and Write scalability Replication and Durability Master-slave replication Replica sets Using write concern for durability Handling replication failures Auto-Sharding How sharding works Setting up a MongoDB shard cluster Choosing a shard key Sharding and indexes Sharding and Replica Set Topologies Administering a sharded cluster Shard / Chunk Migration Backup and Restore Plans Filesystem-based strategies mongodump / mongorestore rsync mongoimport / mongoexport
datastorageoverview Which data storage to choose - from flat files, through SQL, NoSQL to massive distributed systems 7 hours This course helps customer to chose the write data storage depend on their needs. It covers almost all possible modern approaches. File Document Storage (Cloud Storage) Features (OCR, Scalaibility, Search, etc...) Open Source examples (e.g. Next Cloud) Some commercial examples Flat file storage XML databases CSV databases Relational databases Normalization Dependencies and Constrants Scalability - replications, clusters Open Source and commercial software (MySQL, PostrgreSQL, DM7, Oracle, etc..) NoSQL Storage Document Oriented Databases (MongoDB, CouchDB etc...) Column Orientation (Canadra, Scylla etc...) Search Orientation (Elasticsearch... NewSQL CAP Theorem Opensource software (SequoiaDB, etc...) Search Engines Features (text processing, relevancy, etc...) Open Source examples Scalability, High Availability, Load Balacing, etc.... Traditional Datawherehouses Business Inteligence, OLTP and Datawherehouse Opensource and commercial solutions MapReduce and Distributed Parallel Processing Hadoop-like (Hive, HFS, Impala) Distributed filesystem Overview of opensource (Ceph etc...) In-memory Databases Opensource solution (e.g. ApacheIgnite) Others Hypertable (Google Bigtable) BigQuery AWS solutsion (S3, etc...) Beyond present - future trends
mongodbdev MongoDB for Developers 14 hours This course covers everything a database developer needs to know to successfully develop applications using MongoDB. Manipulating Documents Query Insert Update Remove Upsert Removing databases, fields and others Document Structure Datatypes References ID Keys Embedded sub-documents Tree structures Tailable Cursor Two Phase Commits Auto-incrementing Sequence field Aggregation  Distinct Aggregation Pipelines Map-reduce Indexes Default _id Single Field Compound Index Multikey Index Geospatial Index Hashed Index Unique Sparse
aerosdev Aerospike for Developers 14 hours This course covers everything a database developer needs to know to successfully develop applications using Aerospike.Data Management Data Model Primary Index Secondary Index Hybrid Storage Distribution Data Distribution Consistency Guarantees Clustering Cross Data-Center Replication Rack Awareness Client Architecture ACID Key-Value Store Single Record Batch Scans Policies Data Types Lists Maps Geospatial Large Data Types Query User-Defined Functions Record UDF Stream UDF Aggregation Security (Enterprise Edition only) Known Limitations
scylladb Scylla database 21 hours Scylla is an open-source distributed NoSQL data store. It is compatible with Apache Cassandra but performs at significantly higher throughputs and lower latencies. In this course, participants will learn about Scylla's features and architecture while obtaining practical experience with setting up, administering, monitoring, and troubleshooting Scylla.   Audience     Database administrators     Developers     System Engineers Format of the course     The course is interactive and includes discussions of the principles and approaches for deploying and managing Scylla databases and clusters. The course includes a heavy component of hands-on exercises and practice. Introduction to Scylla Installing and running Scylla Scylla's data model and architecture Working with CQL (Cassandra Query Language) Setting up a Scylla cluster Scylla tools Database administration Troubleshooting Scylla
berkeleydb Berkeley DB for developers 21 hours Berkeley DB (BDB) is a software library intended to provide a high-performance embedded database for key/value data. Berkeley DB is written in C with API bindings for C++, C#, Java, Perl, PHP, Python, Ruby, Smalltalk, Tcl, and many other programming languages. Berkeley DB is not a relational database.[1] This course will introduce the architecture and capabilities of Berkeley DB and walk participants through the development of their own sample application using Berkeley DB. Audience     Application developers     Software engineers     Technical consultants Format of the course     Part lecture, part discussion, hands-on development and implementation, tests to gauge understanding Introduction Installing Berkeley DB Configuring Berkeley DB Database operations Working with the Berkeley DB API Creating transactional applications in Berkeley DB Creating concurrent data stores Cursor operations Querying the database Working with indexes Replicating your application Berkeley DB utilities Building, configuring and updating Berkeley DB Backup and recovery Tuning Berkeley DB
accumulo Apache Accumulo: Building highly scalable big data applications 21 hours 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 courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo. Audience     Application developers     Software engineers     Technical consultants Format of the course     Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding Introduction Installing Accumulo Configuring Accumulo Understanding Accumulo's data model, architecture, and components Working with the shell Database operations Configuring your tables Accumulo iterators Developing an application in Accumulo Securing your application Reading and writing secondary indexes Working with Mapreduce, Spark, and Thrift Proxy Testing your application Troubleshooting Deploying your application Accumulo Administrative tasks
voldemort Voldemort: Setting up a key-value distributed data store 14 hours Voldemort is an open-source distributed data store that is designed as a key-value store.  It is used at LinkedIn by numerous critical services powering a large portion of the site. This course will introduce the architecture and capabilities of Voldomort and walk participants through the setup and application of a key-value distributed data store. Audience     Software developers     System administrators     DevOps engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding Introduction Understanding distributed key-value storage systems Voldomort data model and architecture Downloading and configuration Command line operations Clients and servers Working with Hadoop Configuring build and push jobs Rebalancing a Voldemort instance Serving Large-scale Batch Computed Data Using the Admin Tool Performance tuning
neo4j Beyond the relational database: neo4j 21 hours Relational, table-based databases such as Oracle and MySQL have long been the standard for organizing and storing data. However, the growing size and fluidity of data have made it difficult for these traditional systems to efficiently execute highly complex queries on the data. Imagine replacing rows-and-columns-based data storage with object-based data storage, whereby entities (e.g., a person) could be stored as data nodes, then easily queried on the basis of their vast, multi-linear relationship with other nodes. And imagine querying these connections and their associated objects and properties using a compact syntax, up to 20 times lighter than SQL. This is what graph databases, such as neo4j offer. In this hands-on course, we will set up a live project and put into practice the skills to model, manage and access your data. We contrast and compare graph databases with SQL-based databases as well as other NoSQL databases and clarify when and where it makes sense to implement each within your infrastructure. Audience Database administrators (DBAs) Data analysts Developers System Administrators DevOps engineers Business Analysts CTOs CIOs Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development.   Getting started with neo4j neo4j vs relational databases neo4j vs other NoSQL databases Using neo4j to solve real world problems Installing neo4j Data modeling with neo4j Mapping white-board diagrams and mind maps to neo4j Working with nodes Creating, changing and deleting nodes Defining node properties Node relationships Creating and deleting relationships Bi-directional relationships Querying your data with Cypher Querying your data based on relationships MATCH, RETURN, WHERE, REMOVE, MERGE, etc. Setting indexes and constraints Working with the REST API REST operations on nodes REST operations on relationships REST operations on indexes and constraints Accessing the core API for application development Working with NET, Java, Javascript, and Python APIs Closing remarks  
mariadbdev MariaDB 10 Developer Course 28 hours Created DBAs, Administrators and developers who are interested with getting involved in MariaDB 10 based on Linux system. Even beginners, who have the basic skill and knowledge on Linux, can catch up with this course just if you follow the instructor's lab and explanation in detail. This course is intended to practice enough Database Concept and SQL and to show it is very easy to understand how to use SQL and manage MariaDB on Linux system. This course will be delivered to audience with 40% lectures, 50% labs and 10% Q&A. This five-day course strongly emphasizes lab-based activities After this course, you can apply the knowledge, which you obtained through this course, to the other database systems such as MySQL, Oracle Database, MSSQL Server and PostgreSQL as well. It can be deliver on any distribution (Ubuntu, CentOS are commonly used) This course covers these kinds of topics: Chapter 00 MariaDB 10 Developer Course Introduction Chapter 01 MariaDB 10 Introduction Chapter 02 Startup MariaDB 10 Chapter 03 MariaDB Tools - Command & GUI Chapter 04 Retrieving Data using SQL Chapter 05 Filtering Data using SQL Chapter 06 Summarizing, Grouping & Combining Chapter 07 Database, Table & Indexes Chapter 08 Inserting, Updating & Deleting Data Chapter 09 Table Joins Chapter 10 Subqueries Chapter 11 Views Chapter 12 Stored Procedures Chapter 13 Triggers Chapter 14 MariaDB Datatypes Chapter 15 Transaction Processing Chapter 16 MariaDB User Management Chapter 17 MariaDB Client Tools
mongodbau MongoDB for Advanced Users 14 hours This course covers the advanced areas in the use of programming languages related to MongoDB, the goal is for the participant to have the ability to completely master the tool.   Advanced Data Manipulations Adjustment of the Mongo Shell Efficient handling CRUD operations (inserts, queries, updates, deletes) Useful admin commands Performance optimization Built in monitoring tools: mongotop, mongostat Analysing memory and IO performance MongoDB Cloud Manager and Munin Identifying sub-optimal queries. Using the query profiler. Storage engines: MMAPv1 and WiredTiger Explainable object Indexing and special collections Managing indexes and MongoDB indexing internals Single field and compound indexes Indexes on arrays and sub-documents Geo Indexes Capped collections, TTL and tailable cursors Aggregation  Single purpose aggregation Aggregation pipelines Introduction to map-reduce Replication How asynchronous replication works in MongoDB Setting-up and maintaining replica set Using write concern and read preference Handling replication failures Sharding How auto sharding works Setting up a MongoDB shard cluster How to wisely choose a shard key Advanced administering a sharded cluster Managing unbalanced sharded cluster Dealing with chunks (splitting, merging, migrating Security Authentication and authorization in replica sets and sharded clusters Managing privileges and custom roles Recommendations for secure deployment Backup and Restore Plans filesystem based strategies mongodump and mongorestore point-in-time recovery
bigdatastore Big Data Storage Solution - NoSQL 14 hours 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. Limits of Traditional Technologies SQL databases Redundancy: replicas and clusters Constraints Speed Overview of database types Object Databases Document Store Cloud Databases Wide Column Store Multidimensional Databases Multivalue Databases Streaming and Time Series Databases Multimodel Databases Graph Databases Key Value XML Databases Distribute file systems Popular NoSQL Databases MongoDB Cassandra Apache Hadoop Apache Spark other solutions NewSQL Overview of available solutions Performance Inconsitencies Document Storage/Search Optimized Solr/Lucene/Elasticsearch other solutions
hbasedev HBase for Developers 21 hours This course introduces HBase – a NoSQL store on top of Hadoop.  The course is intended for developers who will be using HBase to develop applications,  and administrators who will manage HBase clusters. We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course  is very  hands-on with lots of lab exercises. Duration : 3 days Audience : Developers  & Administrators Section 1: Introduction to Big Data & NoSQL Big Data ecosystem NoSQL overview CAP theorem When is NoSQL appropriate Columnar storage HBase and NoSQL Section 2 : HBase Intro Concepts and Design Architecture (HMaster and Region Server) Data integrity HBase ecosystem Lab : Exploring HBase Section 3 : HBase Data model Namespaces, Tables and Regions Rows, columns, column families, versions HBase Shell and Admin commands Lab : HBase Shell Section 3 : Accessing HBase using Java API Introduction to Java API Read / Write path Time Series data Scans Map Reduce Filters Counters Co-processors Labs (multiple) : Using HBase Java API to implement  time series , Map Reduce, Filters and counters. Section 4 : HBase schema Design : Group session students are presented with real world use cases students work in groups to come up with design solutions discuss / critique and learn from multiple designs Labs : implement a scenario in HBase Section 5 : HBase Internals Understanding HBase under the hood Memfile / HFile / WAL HDFS storage Compactions Splits Bloom Filters Caches Diagnostics Section 6 : HBase installation and configuration hardware selection install methods common configurations Lab : installing HBase Section 7 : HBase eco-system developing applications using HBase interacting with other Hadoop stack (MapReduce, Pig, Hive) frameworks around HBase advanced concepts (co-processors) Labs : writing HBase applications Section 8 : Monitoring And Best Practices monitoring tools and practices optimizing HBase HBase in the cloud real world use cases of HBase Labs : checking HBase vitals
hadoopmapr Hadoop Administration on MapR 28 hours Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand. Big Data Overview: What is Big Data Why Big Data is gaining popularity Big Data Case Studies Big Data Characteristics Solutions to work on Big Data. Hadoop & Its components: What is Hadoop and what are its components. Hadoop Architecture and its characteristics of Data it can handle /Process. Brief on Hadoop History, companies using it and why they have started using it. Hadoop Frame work & its components- explained in detail. What is HDFS and Reads -Writes to Hadoop Distributed File System. How to Setup Hadoop Cluster in different modes- Stand- alone/Pseudo/Multi Node cluster. (This includes setting up a Hadoop cluster in VirtualBox/KVM/VMware, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster). What is Map Reduce frame work and how it works. Running Map Reduce jobs on Hadoop cluster. Understanding Replication , Mirroring and Rack awareness in context of Hadoop clusters. Hadoop Cluster Planning: How to plan your hadoop cluster. Understanding hardware-software to plan your hadoop cluster. Understanding workloads and planning cluster to avoid failures and perform optimum. What is MapR and why MapR : Overview of MapR and its architecture. Understanding & working of MapR Control System, MapR Volumes , snapshots & Mirrors. Planning a cluster in context of MapR. Comparison of MapR with other distributions and Apache Hadoop. MapR installation and cluster deployment. Cluster Setup & Administration: Managing services, nodes ,snapshots, mirror volumes and remote clusters. Understanding and managing Nodes. Understanding of Hadoop components, Installing Hadoop components alongside MapR Services. Accessing Data on cluster including via NFS Managing services & nodes. Managing data by using volumes, managing users and groups, managing & assigning roles to nodes, commissioning decommissioning of nodes, cluster administration and performance monitoring, configuring/ analyzing and monitoring metrics to monitor performance, configuring and administering MapR security. Understanding and working with M7- Native storage for MapR tables. Cluster configuration and tuning for optimum performance. Cluster upgrade and integration with other setups: Upgrading software version of MapR and types of upgrade. Configuring Mapr cluster to access HDFS cluster. Setting up MapR cluster on Amazon Elastic Mapreduce. All the above topics include Demonstrations and practice sessions for learners to have hands on experience of the technology.

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