Certificate in Big Data Technologies offered by Washington University
- Public University
1 Campus- Estd. 1861
Certificate in Big Data Technologies at Washington University Overview
Duration | 8 months |
Total fee | ₹3.31 Lakh |
Mode of learning | Online |
Course Level | UG Certificate |
Certificate in Big Data Technologies at Washington University Highlights
- Earn a certificate from University of Washington
- Get hand on experience of Write code for big data processing
- Configure and deploy frameworks and libraries to run big data software
- Top Employers: Boeing, Microsoft, Amazon, Apple, Phillips, Seattle Genetics
Certificate in Big Data Technologies at Washington University Course details
- For professionals with programming, database and system administration experience who are looking to expand their careers into big data
- The fundamentals of big data stacks, their uses, advantages and limitations
- Key background in distributed systems, cloud computing, relational databases and key-value stores
- The foundations of the Hadoop big data ecosystem and beyond (MapReduce, Hive, Spark, SQL, non-relational processing)
- The pros and cons of batch processing versus in-memory processing
- The uses and limitations of NoSQL stores (HBase, Redis, Elasticsearch, Cassandra, etc.)
- In today's job market, big data is ho and so are data engineers, the professionals who have the knowledge and skills to tame it
- Organizations have a growing need for specialists who know how to design and build platforms that can handle the gigantic amount of data available today
- In this three-course certificate program, explore distributed computing and the practical tools used to store and process data before it can be analyzed
- Work with typical data stacks and gain experience with the kinds of data flow situations commonly used to inform key business decisions
Certificate in Big Data Technologies at Washington University Curriculum
Introduction to data engineering
The fundamentals of modern big data stacks, their uses, advantages and limitations
How functional programming ideas help with building and using systems to store and process big data
The foundations of the Hadoop ecosystem and its emerging successors
The ins and outs of big data processing via multiple paradigms, both storage-bound and in-memory (Spark, Spark SQL, Delta Lake, Hive, SQL)
The origins, uses and limitations of NoSQL stores (HBase, Redis, Elasticsearch, Cassandra, graph-processing systems, etc.)
Building the data pipeline
How a data lake can enhance the usability of your organization's data
Batch and streaming processing using Spark, Flink and other processing tools
How to use Kafka to enable low-latency and real-time processing
Data acquisition and modeling techniques
Pipeline design and integration
Capstone
How to design and implement a data lake for a multichannel retail organization in Azure Data Lake and Azure Databricks using a multi-hop, medallion architecture
Ways to efficiently and performantly ingest, transform and land big data workloads using Apache Spark
How to build a feature data set for a machine learning model
Diagnosis and tuning of common performance pitfalls in Spark jobs
How to design, orchestrate and curate data sets based on business requirements
Certificate in Big Data Technologies at Washington University Faculty details
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