Catalog Search Results
Author
Pub. Date
2017.
Language
English
Description
Learn about use cases and best practices for architecting batch mode applications using big data technologies such as Hive and Apache Spark.
Batch mode consolidates data-related operations in order to reduce the load on networks. Batch mode helps software architects build big data applications that operate smoothly and efficiently under real-world conditions. In this course, you can learn about use cases and best practices for architecting batch...
Author
Pub. Date
2021.
Language
English
Description
Learn the basics of deep learning and get up and running with this technology.
Deep learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use deep learning as their foundational technology. Studying this technology, however, has several challenges. Most learning resources are math-heavy and are difficult to navigate without good math skills. IT professionals need a simplified resource to learn the concepts...
Author
Pub. Date
2022.
Language
English
Description
Get started with MLOps Concepts for Model Development and Integration, to organize machine learning (ML) development and deliver scalable and reliable ML products.
Machine Learning Operations (MLOps) is a fast-growing domain the field of AI. As more models are deployed in production, the need for a structured, agile, end-to-end ML lifecycle with automation has grown multifold. MLOps provides structure to machine learning projects and help them succeed...
Author
Pub. Date
2017.
Language
English
Description
Learn about use cases and best practices for architecting real-time applications using big data technologies, such as Hazelcast and Apache Spark.
Real-time systems have guaranteed response times that can be sub-seconds from the trigger. Meaning that when a user clicks a button, your app better respond—and fast. Architecting applications under real-time constraints is an even bigger challenge when you're dealing with big data. Excessive latency...
Author
Pub. Date
2018.
Language
English
Description
Learn about the stages in business analytics used to predict future events and improve decision-making: predictive analytics, prescriptive analytics, and experimental analytics.
Business analytics encompasses a set of tools, technologies, processes, and best practices that are required to derive knowledge from data. It's an iterative and methodical exploration of data to derive insights from it—and, in turn, make smarter, more strategic decisions...
Author
Pub. Date
2020.
Language
English
Description
Discover how to build scalable and optimized data analytics pipelines by combining the powers of Apache Hadoop and Spark.
Apache Hadoop was a pioneer in the world of big data technologies, and it continues to be a leader in enterprise big data storage. Apache Spark is the top big data processing engine and provides an impressive array of features and capabilities. When used together, the Hadoop Distributed File System (HDFS) and Spark can provide...
Author
Pub. Date
2020.
Language
English
Description
Learn how to use AI to solve common HR challenges, such as recommending training and screening candidates, and to improve employee hiring, satisfaction, and retention.
A global economy and remote workforce have made it difficult for HR departments to track employee satisfaction and motivation. However, using artificial intelligence, data scientists and engineers can now generate powerful insights to improve hiring, training, retention, and more....
Author
Pub. Date
2021.
Language
English
Description
Learn how to make Apache Spark work with other Big Data technologies and put together an end-to-end project that can solve a real-world business problem.
Data engineering is the foundation for building analytics and data science applications in the new Big Data world. Data engineering requires combining multiple big data technologies to construct data pipelines and networks to stream, process, and store data. This course focuses on building full-fledged...
Author
Pub. Date
2018.
Language
English
Description
Learn how to design and build big data pipelines on Google Cloud Platform.
Cloud computing brings unlimited scalability and elasticity to data science applications. Expertise in the major platforms, such as Google Cloud Platform (GCP), is essential to the IT professional. This course—one of a series by veteran cloud engineering specialist and data scientists Kumaran Ponnambalam—shows how to use the latest technologies in GCP to build a big data...
Author
Pub. Date
2020.
Language
English
Description
Discover how to build a real-time stream processing pipeline with Apache Fink. Learn about the platform's windowing, event-time processing, and state management features.
From an engineering perspective, scalability is one of the most pressing challenges in data science. Apache Flink, the powerful and popular stream-processing platform, offers features and functionality that can help developers tackle this challenge. In this course, learn how to...
Author
Pub. Date
2021.
Language
English
Description
Discover how to use Apache Flink and associated technologies to build stream-processing use cases leveraging popular patterns.
Frameworks such as Apache Flink can help you build fast, scalable stream processing applications, but big data engineers still need to design smart use cases to achieve maximum efficiency. In this course, instructor Kumaran Ponnambalam demonstrates how to use Apache Flink and associated technologies to build stream-processing...
Author
Pub. Date
2020.
Language
English
Description
Explore how to build batch mode data pipelines with Apache Flink, the powerful and popular stream-processing platform.
Data engineering is the foundation for enabling analytics and data science applications in the world of big data. It requires building scalable data processing pipelines and delivering them in short time frames. Apache Flink, the powerful and popular stream-processing platform, was designed to help you achieve these goals. In this...
Author
Pub. Date
2018.
Language
English
Description
Discover how to use MySQL to perform common data science tasks. Learn tips and tricks that can help you get more out of MySQL—and write less code in the process.
MySQL is an excellent database for advanced analytics, but few analysts use it that way because they aren't fully aware of its capabilities in this area. Analysts end up writing code to perform common tasks—which can be time-consuming—rather than using MySQL to do the same work. In...
Author
Pub. Date
2019.
Language
English
Description
Learn about the tools and techniques for analyzing text data in R and discover how to perform machine learning and predictions.
Social media, emails, blogs, and text messages offer businesses valuable insights into how their customers think and what they want. But mining this text data isn't a straightforward process; rather, it requires a special set of tools and techniques. In this course, Kumaran Ponnambalam explores these tools and techniques,...
Author
Pub. Date
2019.
Language
English
Description
Learn key techniques for cleansing and processing text in R, and discover how to convert text to a form that's ready for analytics and predictions.
Today’s big data and analytics pipelines are consuming more and more text data generated through websites, social media, and private communications. But deriving insights from text isn't straightforward; it requires a series of techniques and forms for preparing text for analytics and machine learning....
Author
Pub. Date
2018.
Language
English
Description
Learn how to design and build data warehouses using Google Cloud Platform solutions such as BigQuery.
Cloud computing brings unlimited scalability and elasticity to data science applications. Expertise in the major platforms, such as Google Cloud Platform (GCP), is essential to the IT professional. This course—one of a series by veteran cloud engineering specialist and data scientist Kumaran Ponnambalam—shows how to design and build data warehouses...
Author
Pub. Date
2023.
Language
English
Description
Learn about the scalability and manageability aspects of Apache Kafka and how to build asynchronous applications with Kafka and Java.
Scalable and distributed message queuing plays an important role in building real time big data pipelines. Asynchronous publisher/subscriber models are required to handle unpredictable loads in these pipelines. Apache Kafka is the leading technology today that provides these capabilities and is an essential skill for...
Author
Pub. Date
2023.
Language
English
Description
Learn about the fundamental concepts and basic operations of Apache Kafka, a leading technology for real-time streaming capabilities.
One of the key components of a big data processing pipeline is a scalable and distributed message queue. Message queues enable real-time streaming capabilities with multiple producers and consumers of data. This enables real-time applications that can analyze data and produce insights in a scalable fashion. Apache...
Author
Pub. Date
2020.
Language
English
Description
Learn how to use Apache Flink relational APIs—the Table API and SQL—for batch and real-time exploratory data analytics.
Exploratory data analytics is a key phase in data science that deals with investigating data to extract insights. In a world of big data, exploring massive datasets is a challenge, since it requires technologies that are scalable, fast, and feature rich. Apache Flink—the popular stream-processing platform—is well suited...
Author
Pub. Date
2019.
Language
English
Description
Learn how to architect data science solutions at scale using the capabilities provided by Google Cloud Platform (GCP).
Data science is an application area that's exponentially growing, consuming huge amounts of data and making revolutionary predictions. At the same time, Google Cloud Platform (GCP) is fast tracking the cloud movement by providing cutting-edge tools and options. In this course, learn how to architect data science solutions on GCP...
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