Inhalt: 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 course, join Kumaran Ponnambalam as he focuses on how to build batch mode data pipelines with Apache Flink. Kumaran kicks off the course by reviewing the features and architecture of Apache Flink. He then takes a deeper look at the DataSet API and explores various capabilities available for transforming, aggregating, and combining data. To wrap up the course, he presents a use case project that allows you to leverage your new skills. Umfang: 01:07:15.00
Inhalt: 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 for this effort. This course focuses on exploring datasets with SQL on Apache Flink. Instructor Kumaran Ponnambalam starts off by reviewing the relational APIs that Flink provides for big data analytics. Kumaran then takes a deeper look at the Table API and SQL functions. He explores various SQL capabilities available for exploring data, including filtering, aggregations and joins. To wrap up, he provides a use case project that allows you to practice your new skills. Umfang: 01:07:36.00
Inhalt: 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 build a real-time stream processing pipeline with Apache Flink. Instructor Kumaran Ponnambalam begins by reviewing key streaming concepts and features of Apache Flink. He then takes a deeper look at the DataStream API and explores various capabilities available for real-time stream processing, including windowing and joins. After delving into the platform's event-time processing and state management features, he provides a use case project that allows you to put your new skills to the test. Umfang: 01:11:10.00
Inhalt: 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 a big data professional. In this course, Kumaran Ponnambalam provides insights into the scalability and manageability aspects of Kafka and demonstrates how to build asynchronous applications with Kafka and Java. Kumaran starts by demonstrating how to set up a Kafka cluster and explores the basics of Java programming in Kafka. He then takes a deep dive into the various messaging and schema options available. Kumaran also goes over some best practices for designing Kafka applications before finishing with a use case project that applies the lessons covered in the course. Umfang: 01:17:33
Inhalt: 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 Kafka provides these capabilities. As the de facto standard for open-source messaging, Apache Kafka is an essential skill for data scientists, big data engineers, data architects, and solution architects. In this course, instructor Kumaran Ponnambalam introduces Apache Kafka and explains its fundamental concepts and basic operations. Kumaran covers basic concepts like messages, topics, logs, and more. He shows you how to use the Kafka command line, as well as partitions and groups. He goes over Kafka Java programming, then concludes with a use case project. Umfang: 01:18:21
Inhalt: In order to construct data pipelines and networks that stream, process, and store data, data engineers and data-science DevOps specialists must understand how to combine multiple big data technologies. In this course, discover how to build big data pipelines around Apache Spark. Join Kumaran Ponnambalam as he takes you through how to make Apache Spark work with other big data technologies. He covers the basics of Apache Kafka Connect and how to integrate it with Spark for real-time streaming. In addition, he demonstrates how to use the various technologies to construct an end-to-end project that solves a real-world business problem. Umfang: 01:40:14.00
Inhalt: 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. This course explores the ways AI and big data can help HR. Examine three key use cases in the human resources world: predicting employee attrition, mapping collaboration, and creating training recommendations. For each of these use cases, instructor Kumaran Ponnambalam collects and processes data, builds machine learning models, and predicts key outcomes using tools like Python, Jupyter Notebooks, TensorFlow, and Keras. He also briefly explains how to design models to perform other common HR tasks, such as predicting future performance, screening candidates, and even tracking morale. The course concludes with some best practices, including addressing security and privacy concerns particular to HR. Umfang: 01:03:37
Inhalt: IT operations is one of the key business functions for modern enterprises. As data centers become large, distributed, and integrated, the need to monitor and manage hardware, software, networks, and data increases exponentially. And while the elements in a network generate tons of data in terms of logs and events, the need to collect and understand this data to predict future outcomes is also increasing. In this course, learn how to solve common challenges in IT operations using the power of AI. Instructor Kumaran Ponnambalam reviews the key issues that IT ops teams face in their day-to-day operations. He then goes over several uses cases in the world of IT ops, explaining in detail how AI technology can speed up processes like root cause analysis, improve response times at your IT help desk, and more. Along the way, he uses Python, Jupyter Notebooks, Keras, and deep learning techniques to step through practical solutions. Umfang: 01:15:30
Inhalt: 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 mode applications using technologies such as Hive and Apache Spark. There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for data acquisition, transport, processing, storage, and service. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand. Umfang: 01:37:32.00
Inhalt: 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 can cost you money, in terms of system resources consumed and customers lost. Luckily, big data technology and efficient architecture can provide the real-time responsiveness your business needs. In this course, you can learn about use cases and best practices for architecting real-time applications with technologies such as Kafka, Hazelcast, and Apache Spark. There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for real-time streaming, predictive analytics, parallel processing, and pipeline management. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand. Umfang: 01:04:56.00
Inhalt: 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 a truly scalable big data analytics setup. In this course, learn how to leverage these two technologies to build scalable and optimized data analytics pipelines. Instructor Kumaran Ponnambalam explores ways to optimize data modeling and storage on HDFS; discusses scalable data ingestion and extraction using Spark; and provides tips for optimizing data processing in Spark. Plus, he provides a use case project that allows you to practice your new techniques. Umfang: 01:01:55.00
Inhalt: Business analytics allows us to learn from the past and make better predictions for the future. There are three types of analytics used for learning from the past. Descriptive analytics summarizes historical data; exploratory analytics uncovers hidden patterns; and explanatory analytics reveals the reasons for business results. Each type encompasses a different set of tools, technologies, processes, and best practices to derive insights from data. This course by Kumaran Ponnambalam explains why they matter and how and when to use them. He starts by setting the context for business analytics and its various stages. You then explore the stages that focus on the past: descriptive, exploratory, and explanatory. With each stage, you learn about the processes, techniques, and best practices used in the field. Finally, you walk through a use case (the results of an email marketing campaign) that demonstrates how analysis is performed at each stage. Umfang: 00:42:53.00
Inhalt: 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 that are grounded in facts. In this course, learn about the stages in business analytics that are used to predict and build the future-predictive analytics, prescriptive analytics, and experimental analytics. This course dives into each stage, discussing the tools and techniques used for each, as well as best practices leveraged in the field. In addition, the course lends a real-world context to these concepts by using a use case to demonstrate how to execute analytics in each stage. Umfang: 00:42:10.00
Inhalt: Over the past few years, the database world has seen a plethora of new database types appear: document, key-value, graph, and columnar. In addition, data science professionals also have the old giant-relational database management systems (RDBMS)-as an option. Typical data science and analytics use cases have now expanded to text, social media, IoT, and the cloud. With so much to consider, how do you choose the right database for your specific data science project? This course can help by sharing what you need to know to make an informed decision. Kumaran Ponnambalam begins by discussing the roles of databases in data science, as well as the key feature and performance requirements for databases in this field. Next, Kumaran goes over different database types, sharing the strengths and weaknesses of each one. To wrap up, he walks through specific use cases and shows how to select the best database technology for each situation. Umfang: 00:49:58.00
Inhalt: 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 and harness the power of these two technologies for your business. Instructor Kumaran Ponnambalam starts off by reviewing technology options available in GCP for executing various data science processes, as well as the benefits and shortcomings of this suite of cloud computing services. He then analyzes different technologies and steps through the architecture building process for various use cases, including customer analytics and real-time mobile couponing. Umfang: 00:58:25.00
Inhalt: 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 pipeline that ingests, transports, and transforms data entirely in the cloud. Learn how to set up data processing jobs using Apache Beam and Cloud Dataflow. Discover how to leverage Cloud Pub/Sub for stream ingestion and real-time messaging. Finally, find out how to process the stream events in Cloud Dataflow. The course uses an end-to-end use case that shows how to apply the knowledge and best practices from the course in a practical data science workflow. Umfang: 01:07:37.00
Inhalt: 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 using GCP. Explore the different types of storage options available in GCP for files, relational data, documents, and big data, including Cloud SQL, Cloud Bigtable, and Cloud BigQuery. Then learn how to use one solution, BigQuery, to perform data storage and query operations, and review advanced use cases, such as working with partition tables and external data sources. Finally, learn best practices for table design, storage and query optimization, and monitoring of data warehouses in BigQuery. Umfang: 01:00:21.00
Inhalt: 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 cloud engineering specialist and data scientist Kumaran Ponnambalam-shows how to conduct exploratory data analytics with GCP. First, review the concepts of segmentation and profiling. Then get hands on, as you learn to perform both text and visual analysis of data using tools provided by GCP: Cloud Datalab, BigQuery, Cloud Dataflow, and Data Studio. Finally, look at an end-to-end use case that applies what you've learned in the course. Umfang: 00:57:30.00
Inhalt: Predictive analytics use historic data to look forward, enabling organizations to make better decisions. However, making accurate predictions from big data can be an overwhelming task. Enter Google Cloud Platform (GCP), a suite of cloud-computing services that bring scalability, elasticity, and automated machine learning to predictive analytics. This course-one of a series by data scientist Kumaran Ponnambalam-shows how to apply the power of GCP to generate predictions for your business. Start off by exploring the different tools and features for predictive analytics in GCP, including Cloud Dataproc, Cloud ML Engine, and the machine learning APIs such as Cloud Translation, Cloud Vision, and Cloud Video Intelligence. Then explore learn how to build, train, and deploy models to create predictions. Plus, learn best practices for cost control, testing, and performance monitoring of predictive models. Umfang: 00:39:37.00
Inhalt: 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 this course, learn tips and techniques for using MySQL for advanced data analytics. Kumaran Ponnambalam walks through the different stages of analytics, from data ingestion and transformation to generating statistics. In each stage, Kumaran focuses on letting MySQL do the heavy lifting rather than writing code in Java or Python. Discover how to perform data cleansing through MySQL update commands, find peak usage of any resource, perform centering and scaling of data to prepare for machine learning, and more. Plus, Kumaran shows how you can link MySQL with Microsoft Excel to get the best of both worlds. Umfang: 00:41:58.00
Inhalt: Use big data to tell your customer''s story, with predictive analytics. In this course, you can learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey. Start off by learning about the various phases in a customer''s life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review three use cases for predictive analytics in each phase of the customer''s life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up. Umfang: 01:37:58.00
Inhalt: In the world of big data, more and more information is consumed and analyzed in text form. Websites, social media, emails, and chats have become the key sources for data and insights. If you work with data, then understanding how to deal with unstructured text data is essential. In this course, instructor Kumaran Ponnambalam helps you build your text mining skill set, covering key techniques for extracting, cleansing, and processing text in Python. Kumaran reviews key text processing concepts like tokenization and stemming. He also looks at techniques for converting text into analytics-ready form, including n-grams and TF-IDF. Along the way, he provides examples of these techniques using Python and the NLTK library. Umfang: 00:33:31.00
Inhalt: 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. In this course, learn the essential techniques for cleansing and processing text in R, and discover how to convert text to a form that's ready for analytics and predictions. Kumaran Ponnambalam begins by reviewing techniques for extracting, cleansing, and processing text. He then shows how to convert text into an analytics-ready form, including how to use n-grams and TF-IDF. Throughout the course, he provides examples for exercising these techniques using the R and tm libraries. Umfang: 00:55:57.00
Inhalt: Stream processing is rapidly growing in popularity, as more and more data is generated every day by websites, devices, and communications. Platforms such as Apache Kafka Streams 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, get insight into how to solve stream processing problems with Kafka Streams in Java as you learn how to build use cases with popular design patterns. Review some of the significant features of Kafka Streams and discover four popular patterns for stream processing: streaming analytics, alerts and thresholds, leaderboards, and real-time predictions. Along the way, review example use cases, and discover how to leverage Kafka Streams, as well as key technologies like MariaDB and Redis, to implement key examples. Umfang: 01:07:26
Inhalt: Stream processing is becoming more popular as more and more data is generated by websites, devices, and communications. Apache Spark is a leading platform that provides scalable and fast stream processing, but still requires smart design to achieve maximum efficiency. This course helps developers use best practices and validated design patterns to implement stream processing in Apache Spark. Instructor Kumaran Ponnambalam shows how to set up your environment and then walks through four design patterns and real-world use cases: streaming analytics, alerts and thresholds, leaderboards, and real-time predictions. In chapter six, he introduces a start-to-finish project that shows how to go from design to executed job using Spark, Apache Kafka, MariaDB, and Redis. By the end of the course, you'll understand all the capabilities of this powerful platform and be able to incorporate it in your own data engineering solutions. Umfang: 01:09:02
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