“…Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. …”

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“…Understand how to use Amazon Web Service's best-in-class streaming analytics and ML tools Learn about Amazon data pipelines: A very lightweight way to deploy an ML algorithm Explore Redshift and RDS: Databases that stage input data or store model outputs Discover Kinesis: A streaming data ingestion service that performs streaming analytical functions Learn to apply streaming and batch analytical processing to prepare datasets for ML algorithms Gain experience building ML models using Amazon Machine Learning and calling them using Python John Hearty is a data scientist with Relic Entertainment who specializes in using Amazon Web Services to develop data infrastructure and analytics solutions. …”

“…Effectively apply Advanced Analytics to large datasets using the power of PySpark About This Video Solve your big data problems by building powerful Machine Learning models with Spark and implementing them using Python Get up-and-running with Spark's essential libraries and tools (such as PySpark, Spark Streaming, Spark SQL, and Spark MLlib) and learn to apply them in practical, real-world big data applications Leverage Spark 2.x - one of the most popular big data technologies-to discover how powerful Spark Machine Learning is how easily you can apply it! …”

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“…In Detail A comprehensive and new course for learning the Apache Kafka Connect framework with hands-on Training.Kafka Connect is a tool for scalable and reliable streaming data between Apache Kafka and other data systems. …”

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“…Discover how to: * Create goals, measurable objectives, and strategies * Identify the tools and techniques most likely to work for you * Integrate social media and virtual events with existing pre-show, at-show and post-show tactics * Avoid older "virtual" techniques that have been proven to fail * Make the most of live streaming, video interviewing, mobile polling, and other innovations * Expand and enhance your community of customers and prospects year-round * Demonstrate the value of social media to key decision-makers…”

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Tabla de Contenidos: “…3.2 Instruction, bandwidth, and latency bound kernels -- 3.3 Memory bandwidth -- 3.3.1 Theoretical peak bandwidth -- 3.3.2 Effective bandwidth -- 3.3.3 Actual data throughput vs. effective bandwidth -- 4 Synchronization -- 4.1 Synchronization of kernel execution and data transfers -- 4.1.1 Pageable versus pinned host memory -- 4.1.2 Streams -- 4.1.2.1 Creating streams -- 4.1.3 Asynchronous transfers via cudaMemcpyAsync() -- 4.1.4 Synchronization barriers -- 4.1.4.1 cudaDeviceSynchronize() -- 4.1.4.2 cudaStreamSynchronize() -- 4.1.4.3 cudaEventSynchronize() -- 4.1.4.4 Querying streams and events -- 4.1.5 Advanced stream topics -- 4.1.5.1 The default stream -- 4.1.5.2 Non-blocking streams -- 4.1.5.3 Stream priorities -- 4.2 Synchronization of kernel threads on the device -- 4.2.1 Shared memory -- 4.2.2 Synchronizing threads within a block -- 4.2.3 Warps, warp synchronization, and warp-level primitives -- 4.2.3.1 SHFL functions -- 4.2.4 Atomics -- 4.2.5 Memory fences -- 4.2.6 Cooperative groups -- 4.2.6.1 Grid synchronization -- 4.2.6.2 Thread block clusters -- 5 Optimization -- 5.1 Transfers between host and device -- 5.1.1 Pinned memory -- 5.2 Device memory -- 5.2.1 ECC (Error Correcting Code) -- 5.2.2 Global memory -- 5.2.2.1 Declaring global array arguments in kernels -- 5.2.2.2 Coalesced global memory access -- 5.2.3 Local memory -- 5.2.4 Constant memory -- 5.2.5 L1 and L2 caches -- 5.2.6 Shared memory -- 5.2.6.1 Configuring shared memory -- 5.2.6.2 Global memory coalescing through shared memory -- 5.2.6.3 Shared memory bank conflicts -- 5.2.7 Registers -- 5.2.7.1 Exchanging register data between threads in a warp -- 5.3 Execution configuration -- 5.3.1 Thread-level parallelism -- 5.3.2 Instruction-level parallelism -- 5.3.2.1 Asynchronous data transfers between global and shared memory -- 5.3.2.2 Instruction-level parallelism in CUF kernels…”

Tabla de Contenidos: “…Encryption; Streams and Blocks; Block Ciphers; Padding; PKCS#5; Other padding schemes…”

Tabla de Contenidos: “…Deconstructing a Sound for Procedural Use -- Scalability -- Modulation/Modifiers for Variation -- Pitch -- Reuse Sounds for Different Sized Sources -- Transformative Reuse -- Pitch Envelopes -- Volume -- One-shot Volume Envelopes -- Looping Volume Envelopes -- Oscillation -- Wavetables -- Parameterization = Interactivity -- Swapping out Sounds with -Branch- -- Parameters to Pitch with &lt -- Set Pitch Multiplier&gt -- -- Parameter-based Crossfades with -Crossfade by Param- -- Using User-defined Curves -- Monitoring Memory and Level Streaming Approaches -- Asset Checking -- Memory Checking -- Level Streaming -- Sound Streaming -- Conclusion -- 03 Music Part A: Quick Start -- Introduction -- Interactive Music Principles -- Playlists -- Level Music, Loops, and Decay Tails -- Decay Tails -- Loop with Integrated Decay -- Play Then Loop -- Looping Manually -- Stingers/Ornamental Forms -- Simple Parallel Forms -- Simple Transitional Forms -- Conclusion -- 04 Music Part B: Basics and Parallel Forms -- Introduction -- Music Basics -- Source Music -- One-shots -- Music for Timed Events -- Ornamental Forms/Stingers -- Parallel Forms -- Switching or Fading Layers (Parallel Forms for Orientation) -- Using Variables to Determine Volume (Parallel Forms for Proximity) -- Linear Variables to Volume -- Curves to Volume -- Mix Automation for Parallel Layers -- Conclusion -- 05 Music Part C: Transitional Forms -- Introduction -- Clunky Transitions -- Nasty -- Crossfades (and When to Use Them) -- Masking Transitions -- Musical Transitions -- Working with Chunks: Measure-based Transitions -- Decay Tails Revisited -- Controlling a Switch -- Transitioning on Measure in Longer Pieces -- Pre-syncs or Ramps -- Aligned Transitions -- Putting It Together -- A Musical Conclusion -- 06 Dialogue -- Introduction -- Randomization -- Better than Random? …”

Tabla de Contenidos: “…Enterprise JavaBean life cycles -- Stateful session bean life cycle -- Stateless and singleton session bean life cycles -- Message-driven bean life cycle -- EJB timer service -- Calendar-based EJB timer expressions -- EJB security -- Client authentication -- Summary -- Chapter 5: Contexts and Dependency Injection -- Named beans -- Dependency injection -- Qualifiers -- Named bean scopes -- CDI events -- Firing CDI events -- Handling CDI events -- Asynchronous events -- Event ordering -- Summary -- Chapter 6: JSON Processing with JSON-P and JSON-B -- The JSON-P Model API -- Generating JSON data with the Model API -- Parsing JSON data with the Model API -- The JSON-P Streaming API -- Generating JSON data with the Streaming API -- Parsing JSON data with the Streaming API -- JSON pointer -- JSON Patch -- Populating Java objects from JSON with JSON-B -- Generating JSON strings from Java objects with JSON-B -- Summary -- Chapter 7: WebSocket -- Developing a WebSocket server endpoint -- Developing an annotated WebSocket server endpoint -- Developing WebSocket clients -- Developing JavaScript client-side WebSocket code -- Developing WebSocket clients in Java -- Additional information about the Java API for WebSocket -- Summary -- Chapter 8: Java Messaging Service -- Message queues -- Sending messages to a message queue -- Retrieving messages from a message queue -- Browsing message queues -- Message topics -- Sending messages to a message topic -- Receiving messages from a message topic -- Creating durable subscribers -- Summary -- Chapter 9: Securing Java EE Applications -- Identity stores -- Setting up an identity store stored in a relational database -- Setting up an identity store stored in an LDAP database -- Custom identity stores -- Authentication mechanisms -- Basic authentication mechanism -- Form authentication mechanism…”