Big data
We use bigdata technology to extend you business.
Apache Spark is among the fastest-growing tools in data science, due to its streaming, speed, and scalability capabilities. In particular, the Spark MLlib machine learning library is rapidly becoming a must-have for those working in AI.
Kafka is the leading open-source, enterprise-scale data streaming technology. It helps you move your data where you need it, in real time, reducing the headaches that come with integrations between multiple source and target systems.
Hadoop
Extending Hadoop for Data Science: Streaming, Spark, Storm, and Kafka
Big Data Engineering
construct data pipelines and networks that stream, process, and store data,
Data with Kafka
Use Kafka integration with Apache Spark. Write to a HDFS sink.
High-Performance Processing Solutions
Support reporting and analytics for very complex interdependent processing with highly complex business rules
Optimize for lazy evalutation
Spark executes transformation statements only when there is an action executed on the resulting RDDs.
Complex accumulators
Jdai will provide you great flexibility and functionality when you're using Spark.
Big data Engineering
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.
Jdai will help you discover how to build big data pipelines around Apache Spark. how to make Apache Spark work with other big data technologies. how to integrate it with Spark for real-time streaming. how to use the various technologies to construct an end-to-end project that solves business problems.
Spark for Machine Learningand A.I.
Apache Spark is one of the most widely used and supported open-source tools for machine learning and big data. Jdai will help you work with this powerful platform for machine learning. Jdai ues MLlib—the Spark machine learning library—which provides tools for data scientists and analysts who would rather find solutions to business problems than code, test, and maintain their own machine learning libraries.
Jdai will show you how to use DataFrames to organize data structure, and he covers data preparation and the most commonly used types of machine learning algorithms: clustering, classification, regression, and recommendations. By the end of the course, you will have experience loading data into Spark, preprocessing data as needed to apply MLlib algorithms, and applying those algorithms to a variety of machine learning problems.
Spark and MLlib
Spark is a distributed, data processing platform for big data.
Data Prepatration and Transformation
Spark is becoming increasingly polyglot with support for multiple languages.
Clusting
Clustering algorithms group data into clusters that allow us to see how large data sets can break down into distinct subgroups.
Classification
Clustering algorithms group data into clusters that allow us to see how large data sets can break down into distinct subgroups.
We're not the only ones excited happy about help you build Spark data platform
