Hadoop and spark - Study guides, Class notes & Summaries

Test Bank For Business Intelligence, Analytics, Data Science, and AI, 5th Edition by Ramesh Sharda, Dursun Delen, Efraim Turban Chapter 1-11 All Included Latest Update Contents Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science 2 Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 30 Chapter 3 Descriptive Analytics I: Nature of Data, Big Data, and Statistical Modeling 59 Chapter 4 Descriptive Analytics II: Bu...

Big data volume of available data is huge. data keeps growing at staggering rate. data comes from variety of sources in totally different formats Scalable data processing allows database processing systems to cope with the _______, ________, and _________ aspects that big data brings into the system volume, velocity, variety Best data processing system for operational workload (bank, online store, etc) Relational DBMS (Centralized, distributed) Unstructured data (highly available syst...

Data Wrangling, Hadoop and Spark, Big Data Strategy, Data Lakes Midterm 1 Exam Latest Update...

______ is a traditional approach and has established best practices. It is more commonly found in onpremises environments since it was around before cloud platforms. It is a process that involves a lot o data movement, which is something you want to avoid on the cloud if possible due to its resourceintensive nature. - ETL ________ seems similar to ETL at first glance but is better suited to big data scenarios since it leverages the scalability and flexibility of MPP engines like Azure Synapse...

Big Data Questions and Answers Rated A+ What do you know about the term "Big Data"? Big Data is a term associated with complex and large datasets. A relational database cannot handle big data, and that's why special tools and methods are used to perform operations on a vast collection of data. Big data enables companies to understand their business better and helps them derive meaningful information from the unstructured and raw data collected on a regular basis. Big data also allow...

The 3Vs in the definition of Big Data stand for: A: Volume, Value, Veracity B: Volume, Variety, Value C: Volume, Variety, Velocity - correct answer C: Volume, Variety, Velocity The four stages in Big Data adoption identified by the 2012 IBM/University of Oxford report DO NOT include: A: Educate B: Expect C: Engage D: Execute - correct answer B: Expect The main sponsor(s) in the "Execute" stage of big...

HDFS (Hadoop Distributed File System) Open source, Hadoop system that partitions data across many machines. 1 master node + multiple data nodes. Basis for Cloud Storage. MapReduce Hadoop framework for processing large data sets in parallel. 2 step system- first broken down into key/value pairs, then data set is brought back together. YARN Coordinates tasks running on Hadoop cluster and assigns new nodes in case of failure. Consists of resource manager and node manager HIVE Hadoo...

True or false, sources of dat are becoming larger and more diverse True, Billions or even trillions of data sources What is the goal of data processing? To extract data that is useful Why is the volume of data that is available so large? Increasing number of data sources (social media, wearable tech, sensors, cameras, etc), formats, and data points How much data is possibly generated in a day? A petabyte (1 million GB) What is scalable data processing? Allows database processing systems ...

Which service is built on Apache Spark and is compatible with other cloud providers? Select only one answer. Azure Databricks Azure Data Factory Azure Synapse Analytics Azure HDInsight - Answer- Azure Databricks - Databricks is used for processing large amounts of data, which is supported by multiple cloud providers. Data Factory is used to run ETL pipelines. Azure Synapse Analytics is an Azure native service built on Apache Spark. HDInsight is used to process large amounts of data by usi...

AWS Data Engineering Module 2-11 Knowledge checks with Q & A A company is exploring migration Of their on-premises Apache Hadoop workloads to Amazon EMR. What is a benefit Of choosing Amazon EMR instead Of their on-premises Hadoop clusters? ANSWER Amazon EMR likely provides faster provisioning and a larger potential cluster capacity than what most organizations can easily achieve with existing on- premises hardware resources. When launching a cluster, Amazon EMR creates an Amazon EC2 securit...