IBM
NoSQL, Big Data, and Spark Foundations Specialization

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IBM

NoSQL, Big Data, and Spark Foundations Specialization

Springboard your Big Data career. Master fundamentals of NoSQL, Big Data, and Apache Spark with hands-on job-ready skills in machine learning and data engineering.

IBM Skills Network Team
Muhammad Yahya
Romeo Kienzler

Instructors: IBM Skills Network Team

12,195 already enrolled

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Get in-depth knowledge of a subject
4.4

(193 reviews)

Beginner level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.4

(193 reviews)

Beginner level

Recommended experience

1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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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Taught in English

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Specialization - 3 course series

What you'll learn

  • Differentiate among the four main categories of NoSQL repositories.

  • Describe the characteristics, features, benefits, limitations, and applications of the more popular Big Data processing tools.

  • Perform common tasks using MongoDB tasks including create, read, update, and delete (CRUD) operations.

  • Execute keyspace, table, and CRUD operations in Cassandra.

Skills you'll gain

Category: NoSQL
Category: Apache Cassandra
Category: MongoDB
Category: Data Modeling
Category: Query Languages
Category: Scalability
Category: Distributed Computing
Category: Databases
Category: JSON
Category: Database Management
Category: IBM Cloud
Category: Data Manipulation
Category: Database Architecture and Administration

What you'll learn

  • Explain the impact of big data, including use cases, tools, and processing methods.

  • Describe Apache Hadoop architecture, ecosystem, practices, and user-related applications, including Hive, HDFS, HBase, Spark, and MapReduce.

  • Apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL.

  • Use Spark’s RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark’s development and runtime environment options.

Skills you'll gain

Category: Apache Spark
Category: Big Data
Category: Distributed Computing
Category: Apache Hadoop
Category: Debugging
Category: Scalability
Category: Apache Hive
Category: Data Processing
Category: IBM Cloud
Category: PySpark
Category: Data Transformation
Category: Performance Tuning
Category: Docker (Software)
Category: Kubernetes

What you'll learn

  • Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence.

  • Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines.

  • Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML.

  • Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence.

Skills you'll gain

Category: Machine Learning
Category: Apache Spark
Category: Extract, Transform, Load
Category: Supervised Learning
Category: Unsupervised Learning
Category: Data Transformation
Category: PySpark
Category: Data Pipelines
Category: Regression Analysis
Category: Predictive Modeling
Category: Data Processing
Category: Classification And Regression Tree (CART)
Category: Applied Machine Learning
Category: Apache Hadoop
Category: Generative AI

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Instructors

IBM Skills Network Team
IBM
84 Courses1,323,324 learners

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IBM

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