Azure Data Engineering Masters: Build Scalable Solutions

Explore ETL concepts, data warehousing benefits and architecture, data lakes, and the key differences between data warehouses and data lakes for scalable Azure data engineering.

Explore ETL: extract, transform, and load data from diverse sources, cleanse in staging, and load into a data warehouse and data marts for analytics and end users.

Discover what a data warehouse is and how it enables analytics through ETL—extraction, cleaning, transformation, loading, and refreshing of data from diverse sources.

Explore the data warehouse structure, from data sources and staging to the warehouse and data marts, and learn three architectures, ETL, dashboards, and predictive analytics access.

Explore how a data lake stores structured and unstructured data at scale, without structuring it. See dashboards, real-time analytics, and machine learning running on sources, and compare with data warehouses.

Define the target audience for mastering Apache Spark across data scientists, analysts, software engineers, and big data professionals, and outline prerequisites like Python, Scala or Java, SQL, and functional programming.

Trace the origins of Apache Spark, led by Matei Zaharia, and its rise to overcome MapReduce inefficiencies. Learn how Spark enables real-time, iterative, and multi-pass data processing beyond batch workloads.

Explore Apache Spark features: in-memory processing for speed, support for Scala, Java, Python, and R, and a rich ecosystem for batch, streaming, machine learning, and data analytics.

Explore big data concepts, data generation, and traditional storage challenges, and learn how Apache Spark enables scalable data processing and building data applications for Azure data engineering masters.

Explore how big data's velocity and volume drive Apache Spark's distributed processing across clusters for scalable storage, processing, and analytics.

Explore big data dimensions of variety and veracity, covering structured, semi-structured, and unstructured data. Learn to manage text, images, audio, time series, videos, and logs with Spark and other tools.

Explore how big data challenges like code locality and sequential processing drive the use of a distributed file system and parallel processing with Hadoop, MapReduce, and Spark.

Spark serves as a data processing tool in the ETL pipeline. It reads from data storage such as a data lake or database and outputs results for downstream use.

Consult official Spark documentation to learn how to use libraries for structured, semi-structured, machine learning, streaming across local, cluster, or cloud deployments.

Explore how Spark compares to the Hadoop stack, replacing MapReduce, Hive, and Pig, while handling unstructured, structured, and semi-structured data with streaming and machine learning capabilities.

Explore how apache spark outperforms mapreduce by processing data in memory rather than disk, achieving faster iterations on hdfs and enabling spark-based azure tools like synapse and databricks.

Spark Core forms the foundation of Apache Spark, providing distributed data processing with RDDs, in-memory processing, and an expressive API for transformations and actions.

Explore how spark core maximizes data locality, uses partitions and RDDs for parallel processing, and leverages accumulators and broadcast variables, while comparing caching and persistence for memory and disk efficiency.

Explore how Spark builds an execution plan using directed acyclic graphs (DAGs) to orchestrate loads, transformations, and RDDs, with nodes as RDDs and edges as dependencies.

Explain how spark configurations launch the driver, allocate executors, and run RDD transformations and actions through a DAG-driven spark pipeline. Compare library and cluster deployments, notebooks, and quickstart VM options.

Define and apply Spark configurations to enable distributed data processing with Spark context and RDDs, performing read, filter, and map operations on text files.

Explore how spark configurations vary by version, import libraries for PySpark, Python, Java, or Scala, and compare spark context with spark session for unstructured versus structured data.

Explore how to create and manipulate RDDs with PySpark, using spark session and spark context, loading data via parallelize or text file method, and triggering transformations with collect.

learn how to create spark based RDDs with parallelize, differentiate Python variables from PySpark RDDs, and apply map and filter transformations with lambda, culminating in flatMap and collect as actions.

Learn to create and transform RDDs using the text file method in Spark, load data from a data lake, and optimize with map partitions and parallelize.

Azure data engineering masters: explore RDD transformations in Spark to manage partitions, map partitions by index, and perform union, intersection, and distinct with collect-based results for scalable data merging.

Learn how Apache Spark runs as a service on a cluster. Compare notebook versus cluster setups, and explore Cloudera Quickstart VM for practicing Spark with Hadoop storage.

Set up Spark on a cluster with integrated storage to run scalable ETL pipelines, comparing on-prem and cloud stacks, including HDFS, MapReduce, YARN, and cloud data stores.

Size a spark cluster across 6 servers for 100 gb data with a 2:1 ram rule; allocate 30 gb ram per executor with 1–2 cores and 20% os overhead.

Understand the master-slave cluster layout, with drivers, executors, and yarn resource management, and learn to connect from remote offices using putty and winscp to manage cluster jobs.

Explore how spark is installed on a cluster, how PySpark shell triggers, and how configuration files, libraries, and HDFS settings enable spark to run via spark shell and spark submit.

Build a Python spark word count app, read data from distributed storage, tokenize lines, and aggregate word counts, then deploy and run it on the cluster using DevOps tooling.

Execute a word count spark job by creating a spark session, running via spark submit, managing input and output directories, and monitoring the driver and UI on port 4040.

Run a spark word count on the cluster with spark-submit, ingest input from hdfs, save output to hdfs, and package apps for PySpark or Scala.

Explore the PySpark user interface by running a PySpark shell job that loads a text file, tokenizes text, counts word occurrences with reduceByKey, and analyzes DAGs, stages, and storage.

Broadcast variables in spark enable sharing small data across cluster machines to avoid moving large data, referencing with .value during transformations, and they cannot be modified.

Explore Spark SQL, a library built on Spark Core for handling structured and semi-structured data, by writing Spark SQL code, data frame operations, and SQL queries to gain insights.

Spark SQL lets you work with structured and semi-structured data by loading into data frames. It supports CSV and JSON sources, SQL-like queries, and transformations on distributed data sets.

Uncover how the catalyst optimizer parses spark SQL, builds a logical plan, applies rule-based optimizations like predicate pushdown and join reordering, and generates an efficient physical plan.

Explain how the Spark catalyst optimizer converts a SQL query and data frame into optimized logical and physical plans, using cost models and code generation to speed Spark, with tungsten.

Explore how Spark SQL reads data from Hive and various databases, handles semi-structured csv and parquet formats, and uses Spark DataFrames with pandas-like operations for scalable data processing.

Explore Spark SQL fundamentals for reading data from Hive, transforming with DataFrame and DataSet, and using RDD-based operators across Scala, Java, and Python.

Create a Spark session, load a CSV file into a dataframe, enable header true, infer schema, and inspect the dataframe with show and print schema to verify data types.

Learn how to load a CSV into a dataframe, fix headers and data types, and apply Spark SQL filters and selections. Recognize lazy evaluation and DAG-driven transformations.

Sort data with Spark SQL using order by and desc. Import PySpark SQL functions, compute max, and save results to storage for downstream apps.

Utilize Spark SQL to load data into data frames and temporary tables, then query a movie ratings dataset and define top rated movies with business rules.

Compute the most popular movies by counting ratings per movie and join the ratings with the movies dataset on movie id to reveal names, then save the results.

Explore how to analyze polarized movie ratings in Spark SQL, using average and standard deviation to identify highly variable films, compute rating counts, and run SQL queries on temp tables.

Use Spark SQL to run SQL operations on temp views, group by ratings to analyze distributions, and join ratings with the movies dataset to compute averages and analytics.

Learn how to launch a PySpark application on a cluster using spark submit, configure the master and deploy mode, set executor memory, and package the source code for production.

Launch Spark SQL workloads on a cluster, load data from distributed storage, run a Movielens analytics pipeline, and optimize with proper input/output paths, UI monitoring, and parquet outputs.

Explore spark sql concepts for structured and semi-structured data, learn to build applications on a spark-based pipeline, and preview real-time processing with spark streaming.

Explore Spark Streaming for real-time data processing of live data streams, enabling scalable, fault-tolerant analysis without pre-storing data, with use cases in real-time analytics, fraud detection, IoT, and log analytics.

Explore how spark streaming handles real-time data by breaking streams into micro batches and discretized streams as RDDs, applying transformations and actions, with checkpointing.

Explore Spark streaming architecture with input sources like Kafka, Flume, sockets, and file systems, receivers and discretized streams of RDDs, fault tolerance via lineage and checkpoints.

Explore spark streaming architecture for real-time data, capturing continuous input from data sources, breaking it into micro-batches, applying transformations, and saving or routing results to downstream systems.

Explore spark streaming data ingestion from file, kafka, or netcat sources, apply tokenization and word-count transformations, and save results to storage via a streaming context.

Learn how spark structured streaming processes structured data in streams with data frames, treating blocks as rows in an unbounded table and routing results to storage or ingestion tools.

Explore how Databricks runs Spark applications, set up notebooks and clusters, and practice structured streaming with Databricks File System, SQL, Python, and Scala for data science and engineering.

Spark Structured Streaming computes hourly open and close action counts from a streaming data frame using one-hour windows, group by, and in-memory counts tables.

Demonstrate spark streaming with structured example 3 by reading one file at a time, merging outputs into a counts table, and validating streaming data via a netcat socket source.

Explores reading data from a netcat socket and printing it to the console in a spark streaming cluster with a ten-second window, using spark-submit and await termination.

Jump into Spark Streaming and Structured Streaming to process real-time data in micro-batches, apply data frame operations, and push results to storage and downstream applications.

Explore Python’s open source, general purpose nature as a programming and scripting language, its object oriented design, cross platform usability, and role in data science and IoT.

Explore Python data structures—lists, tuples, sets, and dictionaries—and learn how lists are mutable, tuples immutable, with indexing, slicing, nesting, and methods like extend, append, delete, pop, sort, and sorted.

Master Python dictionaries by storing key-value pairs, accessing and updating values, deleting entries, and sorting keys while understanding immutable keys and mutable values.

Explore map, reduce, and filter in Python as paradigms of functional programming to write simpler, shorter code with lambda expressions and practical examples like list transformations and circle area calculation.

Master python control structures and operators, including binary and relational operators. Build decision making with if-else blocks, age and number examples, and input handling.

Explore NumPy, the numerical Python library, for fast array operations, multidimensional ndarrays, and data analytics tasks—from creation with zeros, ones, full, and identity to slicing, indexing, and arithmetic.

Master pandas, a fast Python library for data analytics and manipulation. Read csv files, inspect data frames with head and describe, and perform joins.

Explore data visualization in Python using matplotlib and seaborn to turn numbers into visuals that reveal patterns, correlations, and insights for data-driven decisions.

Explore plotting with matplotlib in Python, learning to create histograms, bar charts, area charts, pie charts, and scatter plots from data frames and csv files.

Install Seaborn with pip, import it as sns, and explore its visualizations from kernel density estimation plots to dist plots, pair plots, and heat maps using iris data.

Master SQL for analytics through a hands-on journey from installation to advanced topics, including constraints, joins, windows functions, stored procedures, ER diagrams, and Python integration with MySQL.

Install MySQL by first installing MySQL Workbench and then the MySQL Installer, setting a root password; learn what a SQL query is and how databases organize data.

Explore SQL, the standard language for relational databases, to read, write, and manage data with commands like select, insert, update, delete, create, and drop, plus integer and varchar data types.

Explore table basics and data definition language (ddl) essentials, including create, alter, and drop table statements, keys and constraints, and how to design and manage relational tables.

Learn the basics of data query language (DQL) with select statements, filtering with where, like, and in, and explore creating and querying tables using SQL commands.

Learn data manipulation language (DML) in SQL, focusing on insert, update, and delete operations, and managing table data as covered in the lecture.

Explore sql date and time functions, including date diff, date format, date add, and sub date, using a transaction details table to format, query, and analyze dates.

Explore regular expressions as a powerful alternative to like in SQL, using regex patterns, character classes, and ranges to match names, emails, and IDs, with practical telco churn examples.

Explore nested queries, or subqueries, using inner and outer queries to combine data from multiple tables, compute averages, and compare totals with SQL.

Learn how to connect to sql databases from python using a python connector like pi mysql, extract data with pandas, perform EDA, and connect to Power BI.

Discover data engineering fundamentals and how to build scalable pipelines for Azure cloud using Databricks, data warehouses, and ETL/ELT patterns. Explore data types, governance, and essential languages like SQL.

Compare ETL and ELT processes, explain data lake loading versus transformation at load time, and contrast data warehousing, star and snowflake schemas in big data contexts.

Explore the shift from monolithic databases to distributed data systems, covering HDFS, MapReduce, and the four v's of big data, plus master-slave cluster architectures for scalability.

Explore devops for data engineering using Azure DevOps pipelines, covering git, environments from dev to prod, and CI/CD to build, test, package, and deploy PySpark code to a data lake.

Explore the fundamentals of Azure cloud for data engineering, including core services, storage, ingestion, processing, and building end-to-end data pipelines with monitoring, cost optimization, and DevOps considerations.

Understand Azure cloud prerequisites by reviewing data engineering fundamentals, cloud concepts, SQL and NoSQL, programming languages, data modeling and warehousing, and security concepts including Unity Catalog and service principals.

Explore how Azure subscriptions and resource groups isolate dev, test, and prod environments and manage costs, while Azure Resource Manager coordinates create, update, and delete of resources.

Learn Azure storage services, including blob storage and Azure Data Lake Storage Gen2, and how storage accounts, containers, and replication enable secure, scalable big data processing with Spark and Synapse.

Implement zero-trust security with RBAC and managed identities, encryption, key vaults, PIM, Purview governance, and monitoring across ADF, Synapse, and Databricks pipelines.

Explore how the Hive metastore stores table metadata, differentiate internal and external tables, and integrate Hive with Spark, Sqoop, and Databricks for scalable analytics.

Explore hive metastore, internal and external tables, and hiveql basics. Understand partitioning, bucketing, and complex data types (array, map, struct) with semi-structured data.