Introduction to Spark Fundamentals
Introduction to Spark Fundamentals Course Online by Checkmate IT Tech offers a transformative journey, elevating your expertise and mastering essential skills. Position yourself for success in the dynamic field of Big Data by enrolling today. Unlock new career opportunities!
- 10+ Courses
- 30+ Projects
- 400 Hours
Introduction to Spark Fundamentals Training is suitable for the following target audiences:
Data Analysts: Data analysts are experts who use Apache Spark to process and examine big datasets in order to get useful insights.
Data Engineers: Data engineers are those in charge of creating and refining data pipelines who wish to use Spark to process data in a scalable manner.
Software developers: Those who want to create programs that need real-time analytics and massive data processing.
Big Data Enthusiasts: People who are interested in pursuing professions in big data and who wish to learn the foundations of Apache Spark.
IT Professionals: To increase performance and efficiency, IT specialists are aiming to integrate Spark into their current data architecture.
Big Data Developer: Using Apache Spark to design and implement big data solutions.
Data Engineer: Using Spark to create scalable data pipelines and ETL procedures for analytics and data warehousing.
Data Scientist: Making use of Spark for predictive modeling, machine learning, and large-scale data analysis.
Cloud Engineer: Using Spark for distributed data processing on cloud platforms like as AWS, Azure, or Google Cloud.
Business Intelligence Analyst: Using Spark SQL to query and report on large data in real time.
Professionals with Spark experience are in high demand in the USA and Canada, where sectors like technology, finance, healthcare, e-commerce, and telecommunications offer competitive pay and opportunities for career advancement in the rapidly expanding field of big data analytics.
- Big Data: What exactly is it
- Drawbacks of MapReduce
- Apache Spark’s features and advantages: an introduction
- Spark Core, SQL, MLlib, Streaming: Overview of an ecosystem
- Use cases of Spark in the actual world.
- Hands-On: Set up Spark locally or with Databricks/Google Colab
- Driver, Executors, Cluster Manager: Spark architecture
- Jobs, phases, assignments: Spark execution model
- RDD (Resilient Distributed Dataset): What, Why, and How
- Comparisons between transformations and actions
- Hands On: Basic RDD operations in PySpark or Scala
- Building RDDs from both internal and outside sources.
- Slum assessment and lineage
- Map, filter, flatMap, reduce by key common RDD transformations
- RDD acts (count, gather, take, saveAsTextFile)
- Hands On: Word count using RDDs
- Definition of Spark SQL
- An overview of dataframes and datasets
- Schema inference and hand-made schema definition
- Doing SQL searches with DataFrames
- Hands On: Load JSON or CSV then query with DataFrames.
- Data Frame transformations (select, filter, groupBy, agg)
- Joins: left, right, inner, outer, left
- optional window functions
- Hands On: Data Frame and join based data analysis
- MLlib’s features and overall capability
- Fundamental ML process in Spark
- Foundation of feature engineering
- Simple linear regression and classification illustration
- Hands On: Utilise MLlib to create a simple ML model
- Batch vs. Stream processing
- Spark structured streaming: what is it?
- Spark helps you to process real-time data.
- Window aggregations
- Hands On: Simple real-time socket/text stream word count
- Final project: mix SQL, ML, and DataFrame chores.
- Review of all the acquired ideas.
- Job preparation advice and certification guides
Note: Curriculum will be modified as per latest industry standards.
This course targets novices, encompassing students, software engineers, and data aficionados seeking to acquire foundational knowledge of Apache Spark and distributed data processing.
Prior experience with Hadoop is not necessary. A fundamental comprehension of Python or Scala, together with data concepts, will enhance your experience in the course.
The course predominantly employs PySpark (Python API for Spark), with supplementary references to Scala for contextual understanding.
Over this 8-week period, you will acquire knowledge of Spark architecture, RDDs, DataFrames, Spark SQL, MLlib (fundamental machine learning), and Structured Streaming.
The course offers the choice to install Spark locally or utilise cloud-based systems such as Databricks or Google Colab, which do not necessitate installation.
The course comprises weekly hands-on labs and coding assignments designed to reinforce theoretical topics through application.
Yes, a final mini-project is scheduled for Week 8, integrating Spark SQL, DataFrames and MLlib to replicate a real-world data pipeline.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
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Upon the successful completion of all modules and the final project, you will be awarded a certificate of completion.
You will utilise Apache Spark, PySpark, Jupyter Notebooks and potentially Databricks for cloud-based applications.
You may investigate advanced Spark subjects, real-time analytics utilising Kafka and Spark, cloud-based Spark solutions (AWS EMR, GCP Dataproc), or delve further into data engineering and machine learning.
We currently offer online sessions with flexible weekday/weekend batches. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
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Job opportunities in USA and Canada
Big Data Developer: Using Apache Spark to design and implement big data solutions.
Data Engineer: Using Spark to create scalable data pipelines and ETL procedures for analytics and data warehousing.
Data Scientist: Making use of Spark for predictive modeling, machine learning, and large-scale data analysis.
Cloud Engineer: Using Spark for distributed data processing on cloud platforms like as AWS, Azure, or Google Cloud.
Business Intelligence Analyst: Using Spark SQL to query and report on large data in real time.
Professionals with Spark experience are in high demand in the USA and Canada, where sectors like technology, finance, healthcare, e-commerce, and telecommunications offer competitive pay and opportunities for career advancement in the rapidly expanding field of big data analytics.
