Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases. Three information-packed courses cover popular NoSQL databases like MongoDB and Apache Cassandra, the widely used Apache Hadoop ecosystem of Big Data tools, as well as Apache Spark analytics engine for large-scale data processing.
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Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases. Three information-packed courses cover popular NoSQL databases like MongoDB and Apache Cassandra, the widely used Apache Hadoop ecosystem of Big Data tools, as well as Apache Spark analytics engine for large-scale data processing.
Big Data Engineers and professionals with NoSQL skills are highly sought after in the data management industry. This Specialization is designed for those seeking to develop fundamental skills for working with Big Data, Apache Spark, and NoSQL databases. Three information-packed courses cover popular NoSQL databases like MongoDB and Apache Cassandra, the widely used Apache Hadoop ecosystem of Big Data tools, as well as Apache Spark analytics engine for large-scale data processing.
You start with an overview of various categories of NoSQL (Not only SQL) data repositories, and then work hands-on with several of them including IBM Cloudant, MonogoDB and Cassandra. You’ll perform various data management tasks, such as creating & replicating databases, inserting, updating, deleting, querying, indexing, aggregating & sharding data. Next, you’ll gain fundamental knowledge of Big Data technologies such as Hadoop, MapReduce, HDFS, Hive, and HBase, followed by a more in depth working knowledge of Apache Spark, Spark Dataframes, Spark SQL, PySpark, the Spark Application UI, and scaling Spark with Kubernetes. In the final course, you will learn to work with Spark Structured Streaming Spark ML - for performing Extract, Transform and Load processing (ETL) and machine learning tasks.
This specialization is suitable for beginners in the fields of NoSQL and Big Data – whether you are or preparing to be a Data Engineer, Software Developer, IT Architect, Data Scientist, or IT Manager.
What's inside
Three courses
Introduction to NoSQL Databases
(0 hours)
Introduction to NoSQL Databases
(0 hours)
Introduction to Big Data with Spark and Hadoop
(0 hours)
Introduction to Big Data with Spark and Hadoop
(0 hours)
Machine Learning with Apache Spark
(0 hours)
Machine Learning with Apache Spark
(0 hours)Learning objectives
- Work with nosql databases to insert, update, delete, query, index, aggregate, and shard/partition data.
- Develop hands-on nosql experience working with mongodb, apache cassandra, and ibm cloudant.
- Develop foundational knowledge of big data and gain hands-on lab experience using apache hadoop, mapreduce, apache spark, spark sql, and kubernetes.
- Perform extract, transform and load (etl) processing and machine learning model training and deployment with apache spark.
- Work with nosql databases to insert, update, delete, query, index, aggregate, and shard/partition data.
- Develop hands-on nosql experience working with mongodb, apache cassandra, and ibm cloudant.
- Develop foundational knowledge of big data and gain hands-on lab experience using apache hadoop, mapreduce, apache spark, spark sql, and kubernetes.
- Perform extract, transform and load (etl) processing and machine learning model training and deployment with apache spark.
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