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MongoDB Next Steps

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An introduction to ETL operations and the aggregation framework

Topic: Data
Axel Sirota

Document-based database MongoDB allows you to perform simple to complicated queries in just a couple of lines, including full-text search, geospatial queries, or even complex ETL (extract, transform, load) operations. It’s fast, scalable, reliable, schema-less, ACID-compliant, and so flexible you can visualize data as JSON files and write queries, transformations, and transactions as code—all of which makes MongoDB incredibly easy to debug and fast to develop with.

Expert Axel Sirota walks you through two of MongoDB’s unique features—full-text search and geospatial queries—putting special emphasis in the relationship of the index structure and the final query structure. You’ll discover how to bring horizontal scalability and high availability to MongoDB with sharding strategies and replication deployments. If you already have a handle on MongoDB basics, join in to learn how to boost performance and make the most out of its more advanced features.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • How to expand your MongoDB deployment to a multinode cluster with high availability
  • How to use MongoDB’s text indexes and make the most complex full-text queries in multiple languages
  • How to use MongoDB’s geo indexes and query for close objects with its longitude and latitude

And you’ll be able to:

  • Create full-text search queries in multiple languages and with diacritic symbols
  • Create aggregation pipelines that involve knowing the global distance between two documents
  • Deploy a highly available MongoDB cluster and configure its sharding and acknowledgement to ensure fault tolerance

This training course is for you because...

  • You’re a data engineer who needs to provide complex data pipelines in which index optimization is key for performance.
  • You’re a software engineer who needs to provide distance-aware, full-text features to your application.
  • You’re a DBA who wants to scale your MongoDB deployment 10x.

Prerequisites

  • A working knowledge of MongoDB, equivalent to MongoDB First Steps (live online training course with Axel Sirota)

Recommended preparation:

Recommended follow-up:

About your instructor

  • Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Machine Learning Operations. After researching in Probability, Statistics and Machine Learning optimisation, he is currently working at JAMPP as a Machine Learning Research Engineer leveraging customer data for making accurate predictions at Real Time Bidding.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Full-text search in MongoDB (60 minutes)

  • Presentation: The $text operator and its fields; text indexes in MongoDB
  • Katacoda interactive exercises: Creating your first full-text search query in MongoDB; advanced operations with full-text search in MongoDB
  • Q&A

Break (10 minutes)

Geospatial queries in MongoDB (50 minutes)

  • Presentation: The $near operator and its fields; geo indexes in MongoDB
  • Katacoda interactive exercise: Finding nearest restaurants with geospatial queries in MongoDB
  • Hands-on exercise and live demo: Simulating a scenario where a person is missing, add a spatial index over your dataset, construct the geospatial query, and add it to an aggregation pipeline to clean your data in order for it to be usable
  • Q&A

Break (10 minutes)

High availability in MongoDB (50 minutes)

  • Presentation: Replication in MongoDB—primary and secondary architecture; read set and write acknowledgement in replica sets
  • Group discussion: Election strategies for primary election
  • Hands-on exercises and live demo: Deploy a three-node replica set; answer situational questions on how reads and writes would work with different replication systems
  • Q&A

Break (10 minutes)

Horizontal scalability in MongoDB: Sharding (40 minutes)

  • Presentation: What is sharding, and how does it enable horizontal scalability? What does a sharded cluster look like?
  • Hands-on exercise and live demo: Set up a sharded cluster and within it a sharded collection in order to query over it with aggregations

Wrap-up and Q&A (10 minutes)