“…Dive deep into data science technologies, including Spark, TensorFlow, and the Jupyter Notebook Learn how Spark and Python notebooks enable data scientists and developers to work together Explore how the notebook environment works with Spark SQL for structured data Use notebooks and Spark as a launchpad to pursue supervised, unsupervised, and deep learning data models Learn additional Spark functionality, including graph analysis and streaming Explore the use of analytics in the production environment, particularly when creating data pipelines and deploying code…”

Tabla de Contenidos: “…Private subnet -- Bastion subnet -- Bastion host -- Security groups -- Infrastructure as Code -- Setting up CloudFormation -- Centralized logging -- Setting up CloudWatch -- Summary -- Chapter 8: Designing a Big Data Application -- Introducing big data applications -- AWS components used in big data applications -- Analyzing streaming data with Amazon Kinesis -- Best practices for serverless big data applications -- Best practices for using Amazon EMR -- Understanding common EMR use cases -- Lowering EMR costs -- Using Amazon EC2 Spot and Auto Scaling -- Best practices for distributed machine learning and predictive analytics -- Using Amazon SageMaker for machine learning -- Understanding Amazon SageMaker algorithms and features -- Security overview for big data applications -- Securing the EMR cluster -- Encryption -- Authentication -- Authorization -- Securing serverless applications -- Understanding serverless application authentication and authorization -- Configuring and using EMR-Spark clusters -- Summary -- Chapter 9: Implementing a Big Data Application -- Setting up an Amazon Kinesis Stream -- Creating an AWS Lambda function -- Using Amazon Kinesis Firehose -- Using AWS Glue and Amazon Athena -- Using Amazon SageMaker -- Summary -- Chapter 10: Deploying a Big Data System -- Using CloudFormation templates -- Creating a data lake using a CloudFormation template -- Authoring and deploying serverless applications -- Understanding AWS SAM -- Understanding the SAM template -- Introducing SAM Local -- Developing serverless applications using AWS Cloud9 -- Automating serverless application deployments -- Using AWS Serverless Application Repository -- Summary -- Other Books You May Enjoy -- Leave a review - let other readers know what you think -- Index…”

Tabla de Contenidos: “…Selecting algorithms based on function -- Using Spark to generate real-time big data analytics -- Chapter 4 Math, Probability, and Statistical Modeling -- Exploring Probability and Inferential Statistics -- Probability distributions -- Conditional probability with Naïve Bayes -- Quantifying Correlation -- Calculating correlation with Pearson's r -- Ranking variable-pairs using Spearman's rank correlation -- Reducing Data Dimensionality with Linear Algebra -- Decomposing data to reduce dimensionality -- Reducing dimensionality with factor analysis -- Decreasing dimensionality and removing outliers with PCA -- Modeling Decisions with Multiple Criteria Decision-Making -- Turning to traditional MCDM -- Focusing on fuzzy MCDM -- Introducing Regression Methods -- Linear regression -- Logistic regression -- Ordinary least squares (OLS) regression methods -- Detecting Outliers -- Analyzing extreme values -- Detecting outliers with univariate analysis -- Detecting outliers with multivariate analysis -- Introducing Time Series Analysis -- Identifying patterns in time series -- Modeling univariate time series data -- Chapter 5 Grouping Your Way into Accurate Predictions -- Starting with Clustering Basics -- Getting to know clustering algorithms -- Examining clustering similarity metrics -- Identifying Clusters in Your Data -- Clustering with the k-means algorithm -- Estimating clusters with kernel density estimation (KDE) -- Clustering with hierarchical algorithms -- Dabbling in the DBScan neighborhood -- Categorizing Data with Decision Tree and Random Forest Algorithms -- Drawing a Line between Clustering and Classification -- Introducing instance-based learning classifiers -- Getting to know classification algorithms -- Making Sense of Data with Nearest Neighbor Analysis -- Classifying Data with Average Nearest Neighbor Algorithms…”

Tabla de Contenidos: “…Time Boxing -- Productivity Apps -- Calendar Apps -- Google Calendar -- Calandly -- Woven -- Reclaim -- Clockwise -- Lightpad -- Automation -- Zapier -- IFTTT -- Tray.io -- DocuSign -- Hootsuite -- Two-Way and Group Communication -- Loom -- Yac -- Pragli -- Sococo -- Distraction Blocking -- RescueTime -- Freedom -- Forest -- Spark -- Time Tracking Software -- Toggl Track -- Everhour -- Harvest -- TMetric -- Monitoring Remote Workers -- Fear of Loss Overrides Trust -- Ramifications of Monitoring Workers -- Nobody Wants to Work for Big Brother -- Transparency Is the Best Strategy -- Company Culture and Trust -- Monitoring Software -- Prodoscore -- Hubstaff -- Time Doctor -- Time Managing as a Whole -- Chapter 6 Group Tasks: Keeping the Teamwork in Your Team -- Remote Work Fatigue -- Lowered Work/Life Balance -- Mental Health -- Perform a Reset -- Encourage Boundaries -- Know When to Log Off -- Turn Off Emails -- Encourage Breaks -- Foster an Environment of Engagement -- Expenses for Home Office Needs -- Gratitude -- Collaboration Tools and Usage -- Too Many Video Meetings -- No Back-to-Back Meetings -- Schedule a Meeting-Free Day Each Week -- Pick Up the Phone -- Do You Need the Meeting? …”

Tabla de Contenidos: “…Describing the Context of Data Science Programming Languages -- Unfolding Open Source Frameworks for AI/ML Models -- TensorFlow -- Theano -- Torch -- Caffe and Caffe2 -- The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK) -- Keras -- Scikit-learn -- Spark MLlib -- Azure ML Studio -- Amazon Machine Learning -- Choosing Open Source or Not? …”

Tabla de Contenidos: “…Apache Projects related to big data -- Apache Zookeeper -- Apache Kafka -- Apache Flume -- Apache Cassandra -- Apache HBase -- Apache Spark -- Summary -- Chapter 4: NoSQL Database -- What is NoSQL? …”

Tabla de Contenidos: “…-- Keeping up with the FCC -- Renewing your license -- Keeping the light on -- Partying at Your Official FM Launch -- Chapter 5 Weaving Your Webcast -- Launching Your Station on the Infinite Dial -- Connecting with a stream host -- Getting a direct URL -- Embedding player buttons on your website -- Utilizing pop-up players -- Adding the audio element -- Registering with Online Radio Directories -- Developing Apps for Listeners on The Go -- Teaching Skills to Smart Speakers: "Alexa, Play WXOX" -- Part 3 Staying in Orbit: Building a Station That Endures -- Chapter 6 Where the Magic Happens: Building a Studio with a Spark! -- Finding a Studio Location -- Considering Interior Spaces -- Soundproofing -- Sound dampening -- Installing Studio Furniture -- Selecting Studio Equipment -- Audio console -- Microphones -- Mic booms -- On-air light -- Headphones -- Headphone amp -- Studio monitors -- Turntables -- CD players -- Cassette players -- Computers -- Webcaster -- Archiver -- Player -- Computer cables -- Accessories and adapters…”

Tabla de Contenidos: “…Crest-climbing insight No. 4: Your flawed fear needs a free ride home -- Crest-climbing insight No. 5: You can't climb all the hills today -- Great social impact communicators build a resilient mindset to keep going in the face of adversity -- Act with integrity -- Lead with humility -- Be scientific in your approach -- Stay skeptical -- Communicate with transparency and openness -- After your hike past the crest, learn to navigate the cloud forest beyond -- Wait -- Analyze -- Take Gratitude -- Evaluate Options -- Run Again -- The watchout and the opportunity -- Key Questions -- Chapter 6: Secret #6: Highly effective social impact communicators ... cede perfection to the messy reality of change-making -- How messy looks for social impact communicators -- Paint the vision, not Van Gogh -- Why you should embrace satisfied persistence rather than perfection -- Stop trying to tie up all the loose ends -- Move ahead instead of around in circles -- When the dust flies, remember it will settle -- Capture attention and direct focus rather than sowing chaos -- Turn panic into peaceful action -- Guard the silent moments -- Engage in regular conversation that sparks ideas and opportunities -- Trade predictability for adaptability -- Key Questions -- Chapter 7: Secret #7: Highly effective social impact communicators ... build personal and professional legacies that outlive them and their careers -- First principles of legacy building for social impact communicators -- Principle No. 1: Legacy is the ripple effect of our actions -- Principle No. 2: Legacy is shiftable, not set in stone -- Principle No. 3: Legacy exists beyond our lifetime -- Principle No. 4: Legacy can be a blessing or a byword -- Principle No. 5: Legacy persists in the background -- How you can create a legacy that outlives you -- Legacy Builder 1: Give away your grace and mercy liberally…”

“…With this concise book, you’ll learn how to use Python with the Hadoop Distributed File System (HDFS), MapReduce, the Apache Pig platform and Pig Latin script, and the Apache Spark cluster-computing framework. Authors Zachary Radtka and Donald Miner from the data science firm Miner & Kasch take you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. …”

Tabla de Contenidos: “…Scrum Values Expose Fear, Dysfunction, and Waste -- Prepare for change aches and pains -- The five core values of Scrum -- Scrum value #1 - Courage -- Free the spark -- Scrum value #2 - Commitment -- Commitment exposes fear of dedicated, cross-functional Scrum teams -- Expert-to-Task or ETT model of human resource allocation -- Team-to-Backlog or the TTB model of human team allocation -- What do we do about commitment issues? …”

Tabla de Contenidos: “…Creating an IAM policy and role for DMS -- Configuring DMS settings and performing a full load from MySQL to S3 -- Querying data with Amazon Athena -- Hands-on - ingesting streaming data -- Configuring Kinesis Data Firehose for streaming delivery to Amazon S3 -- Configuring Amazon Kinesis Data Generator (KDG) -- Adding newly ingested data to the Glue Data Catalog -- Querying the data with Amazon Athena -- Summary -- Chapter 7: Transforming Data to Optimize for Analytics -- Technical requirements -- Overview of how transformations can create value -- Cooking, baking, and data transformations -- Transformations as part of a pipeline -- Types of data transformation tools -- Apache Spark -- Hadoop and MapReduce -- SQL -- GUI-based tools -- Common data preparation transformations -- Protecting PII data -- Optimizing the file format -- Optimizing with data partitioning -- Data cleansing -- Common business use case transformations -- Data denormalization -- Enriching data -- Pre-aggregating data -- Extracting metadata from unstructured data -- Working with Change Data Capture (CDC) data -- Traditional approaches - data upserts and SQL views -- Modern approaches - Open Table Formats (OTFs) -- Apache Iceberg -- Apache Hudi -- Databricks Delta Lake -- Hands-on - joining datasets with AWS Glue Studio -- Creating a new data lake zone - the curated zone -- Creating a new IAM role for the Glue job -- Configuring a denormalization transform using AWS Glue Studio -- Finalizing the denormalization transform job to write to S3 -- Create a transform job to join streaming and film data using AWS Glue Studio -- Summary -- Chapter 8: Identifying and Enabling Data Consumers -- Technical requirements -- Understanding the impact of data democratization -- A growing variety of data consumers -- How a data mesh helps data consumers…”

“…Combine SQL Server for data, PolyBase for data virtualization, Data Pools for scale-out data processing, and Spark with HDFS for scalable non-relational data processing and analysis. …”

“…You'll enjoy overviews of technologies and platforms like Microsoft’s Azure HDInsight, O'Reilly's Oriole, BMC's Control-M, Google's TensorFlow, AWS Lambda, Twitter's Heron, Intel's BigDL, as well as talks on almost every Apache ecosystem tool available today, including Arrow, Beam, Cassandra, Druid, Flink, Flume, HAWQ, Hive, Impala, Kafka, Kinetica, Kudu, MXNet, Presto, Spark, Spark ML, Spark Streaming, and Spark SQL. …”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- About the Book -- About the Editors -- List of Contributors -- Preface -- 1 The Future of Business Management with the Power of Distributed Systems and Computing -- 1.1 Introduction -- 1.1.1 Distributed Systems in Business Management -- 1.2 Understanding Distributed Systems and Computing -- 1.2.1 Definition of Distributed Systems and Computing -- 1.2.2 Advantages for Business Management -- 1.2.3 Characteristics of Distributed Systems and Computing for Business Management -- 1.3 Applications of Distributed Systems and Computing in Business Management -- 1.3.1 Inventory Management and Supply Chain Optimization -- 1.3.2 Customer Relationship Management -- 1.3.3 Financial Management and Accounting -- 1.3.4 Data Analytics and Decision-Making -- 1.3.5 Collaboration and Communication Within and Across Organizations -- 1.4 Limitations of Distributed Systems in Business Management -- 1.4.1 Security and Privacy Concerns -- 1.4.2 Technical Issues and Maintenance -- 1.4.3 Organizational and Cultural Challenges -- 1.4.4 Legal and Regulatory Compliance -- 1.5 Future Developments and Opportunities -- 1.5.1 Potential Future Developments and their Implications for Business Management -- 1.5.2 Opportunities for Research and Innovation in the Field -- 1.6 Conclusion -- References -- 2 Applications of Optimized Distributed Systems in Healthcare -- 2.1 Introduction -- 2.2 Literature Survey -- 2.2.1 Need for Optimization of Distributed Systems -- 2.2.2 Performance Optimization of Distributed Systems -- 2.2.3 Characteristics of Optimized Distributed Systems in Healthcare -- 2.2.4 Applications of Optimized Distributed Systems in Healthcare -- 2.2.5 Technologies Being Used in Healthcare -- 2.2.5.1 Spark -- 2.2.5.2 Hadoop -- 2.3 Real Cases -- 2.4 Conclusion -- References…”