This document provides an overview and introduction to NoSQL databases. It discusses key-value stores like Dynamo and BigTable, which are distributed, scalable databases that sacrifice complex queries for availability and performance. It also explains column-oriented databases like Cassandra that scale to massive workloads. The document compares the CAP theorem and consistency models of these databases and provides examples of their architectures, data models, and operations.
The document discusses factors to consider when selecting a NoSQL database management system (DBMS). It provides an overview of different NoSQL database types, including document databases, key-value databases, column databases, and graph databases. For each type, popular open-source options are described, such as MongoDB for document databases, Redis for key-value, Cassandra for columnar, and Neo4j for graph databases. The document emphasizes choosing a NoSQL solution based on application needs and recommends commercial support for production systems.
Big Data Processing in Cloud Computing EnvironmentsFarzad Nozarian
油
This is my Seminar presentation, adopted from a paper with the same name (Big Data Processing in Cloud Computing Environments), and it is about various issues of Big Data, from its definitions and applications to processing it in cloud computing environments. It also addresses the Big Data technologies and focuses on MapReduce and Hadoop.
Big Data Processing in Cloud Computing EnvironmentsFarzad Nozarian
油
This is my Seminar presentation, adopted from a paper with the same name (Big Data Processing in Cloud Computing Environments), and it is about various issues of Big Data, from its definitions and applications to processing it in cloud computing environments. It also addresses the Big Data technologies and focuses on MapReduce and Hadoop.
Andrew Erlichson completed a MongoDB course called M101P: MongoDB for Developers on September 28, 2015, according to a certificate from MongoDB, Inc. signed by Ali Hallaji, Vice President of Education. The authenticity of the certificate can be verified online at the provided URL.
Andrew Erlichson completed a MongoDB course called M102: MongoDB for DBAs on September 25, 2015, as confirmed in a certificate from MongoDB, Inc. where he serves as Vice President of Education. The authenticity of the certificate can be verified online at the provided URL.
Andrew Erlichson completed a course of study in M202: MongoDB Advanced Deployment and Operations offered by MongoDB, Inc. on September 25, 2015, as confirmed in a certificate of completion. The authenticity of this certificate can be verified online at the provided URL.
Large Data Analyze with PyTables,
This presentation has been collected from several other presentations(PyTables presentation).
For more presentation in this field please refer to this link (http://pytables.org/moin/HowToUse#Presentations).
This document discusses using PyTables to analyze large datasets. PyTables is built on HDF5 and uses NumPy to provide an object-oriented interface for efficiently browsing, processing, and querying very large amounts of data. It addresses the problem of CPU starvation by utilizing techniques like caching, compression, and high performance libraries like Numexpr and Blosc to minimize data transfer times. PyTables allows fast querying of data through flexible iterators and indexing to facilitate extracting important information from large datasets.
This document discusses using PyTables to analyze large datasets. PyTables is built on HDF5 and uses NumPy to provide an object-oriented interface for efficiently browsing, processing, and querying very large amounts of data. It addresses the problem of CPU starvation by utilizing techniques like caching, compression, and high performance libraries like Numexpr and Blosc to minimize data transfer times. PyTables allows fast querying of data through flexible iterators and indexing to facilitate extracting important information from large datasets.