Spark For Data Engineers

Trainer(s): Tomaž Kaštrun
Provider: DPS 2022 (Data Platform Summit)
Duration: 8 Hours
Subtitles: Yes
Regular Price: USD 299
Offer Price: USD 74.75 (75% OFF)
Discount Code: HAPPY75
(GST + Internet Transaction Fee additional)
Subscription Period: Lifetime Access


Data Analysts, data Scientist, Business Intelligence analysts and many other roles require data on demand. Fighting with data silos, many scatter databases, Excel files, CSV files, JSON files, APIs and potentially different flavours of cloud storage may be tedious, nerve-wracking and time-consuming.

Automated process that would follow set of steps, procedures and processes take subsets of data, columns from database, binary files and merged them together to serve business needs and potentials is and still will be a favorite job for many organizations and teams.

Apache Spark™ is designed to to build faster and more reliable data pipelines, cover low level and structured API and brings tools and packages for Streaming data, Machine Learning, data engineering and building pipelines and extending the Spark ecosystem.

Spark is an absolute winner for this tasks and a great choice for adoption.

Data Engineering should have the extent and capability to do:

– System architecture
– Programming
– Database design and configuration
– Interface and sensor configuration

And in addition to that, it is as important as familiarity with the technical tools is, the concepts of data architecture and pipeline design are even more important. The tools are worthless without a solid conceptual understanding of:

– Data models
– Relational and non-relational database design
– Information flow
– Query execution and optimisation
– Comparative analysis of data stores
– Logical operations

Apache Spark have all the technology built-in to cover these topics and has the capacity for achieving a concrete goal for assembling together functional systems to do the goal.


  1. Getting to know Apache Spark, Installation and setting up the environment
  2. Creating Datasets, organising raw data and working with structured APIs
  3. Designing and building pipelines, moving data and building data models with Spark
  4. Data and process orchestration, deployment and Spark Applications
  5. Data Streaming with
  6. Ecosystem, tooling and community