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.

Learn cloud-based ETL tools, focusing on Azure Data Factory to create data-driven pipelines that ingest from diverse sources and orchestrate transformation toward Azure Synapse and Power BI.

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

Leverage a data warehouse to improve data analytics and faster decisions by standardizing data for business intelligence tools like Power BI and Tableau.

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.

Understand why staging environments matter for data warehousing by enabling safe extraction within time slots and cross-system joins before loading to the data warehouse and data marts.

Learn how data marts provide department-specific data storage drawn from a data warehouse to enable faster analytics and reduced latency, with a smaller size than warehouses.

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.

Compare data lakes and data warehouses, highlighting sources, structure, and schema handling, and see why data lakes support predictive analytics with structured and unstructured data.

Explore the elements of data lakes, including data movement, secure storage, analytics, and machine learning, and how they enable real-time data import, relational and non-relational storage, and predictive insights.

Master Apache Spark for scalable big data processing, analytics, and machine learning. Gain fundamentals to advanced techniques, including Spark SQL, streaming, and graph processing, with performance tuning.

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.

Explore Apache Spark, its in-memory fast cluster computing, and its workloads for batch, real-time, interactive queries, iterative algorithms, and machine learning.

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 why Apache Spark outperforms MapReduce with in-memory processing, enabling faster batch, interactive queries, real-time streams, and machine learning workflows.

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 big data capabilities by analyzing transactional data from banks and retail platforms, multimedia and sensor data, public and external sources to reveal insights for business operations and trends.

Explore big data storage across diverse sources from structured relational databases to application servers and unstructured files, and learn how to store and process data with tools like spark.

Examine big data problems in data processing, from code locality to sequential processing with a single process. Learn how parallel processing with multiple processes speeds up workloads for large datasets.

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.

Learn how big data problems arise with traditional systems and how an ETL pipeline using spark integrates data ingestion, storage, and processing from sources like users, products, and clickstreams.

Explore how to build a data pipeline that combines database and clickstream data for analytics using big data processing, semi-structured logs, and Spark.

Build an ETL pipeline by moving data from databases and application server logs into a data lake on a distributed file system, using spark for fast processing.

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.

Learn how Apache Spark processes data through an ETL workflow and explore Spark availability across local machines, clusters, and cloud environments using PySpark and Maven-based libraries.

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

Explore the Hadoop ecosystem, from data ingestion with Sqoop and Flume to storage with HDFS and HBase, and learn how Spark replaces MapReduce or works with Hive and Pig.

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 the Spark architecture from Spark Core with driver and executors to libraries like Spark SQL, machine learning, and graphics streaming, and how Yarn or Mesos manage the jobs.

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 Spark Core concepts like RDDs, in-memory processing, and directed acyclic graphs to optimize the execution of transformations and actions, with lazy evaluation inspired by Pig.

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.

Master data locality and partitioning in spark core to minimize data movement, boost parallelism, and efficiently manage shared variables like accumulators and broadcast; learn caching and persistence levels for performance.

Understand the spark core's fundamental object: the resilient distributed dataset, its immutability and partitioning, and how lineage enables recomputing rdds after transformations.

Explore how Spark's RDDs are immutable, lazily evaluated, and recoverable via lineage, and learn practical methods to create RDDs from text files, existing RDDs, or parallelize.

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.

Explore how a dag represents the flow of read, filter, map, and write as nodes and edges. See how it helps generate a physical execution plan for distributed processing.

Understand how spark-submit launches a driver, builds the dag from transformations and actions, allocates resources via a cluster manager like Yarn, distributes tasks to executors, and monitors data movement.

Understand spark application execution: the driver creates a dag, requests resources from yarn, coordinates executors, minimizes data shuffling, applies transformations and actions, and produces final output.

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 spark configurations, focusing on spark context for unstructured data, creating rdds with sc.textFile and sc.parallelize, and applying transformations while noting version stability (1.6, 2.x, 3.x) for spark.

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 run Spark applications in Google Colab by installing PySpark, importing libraries, and using notebook cells to connect to a backend and test Spark workflows.

Connect the notebook to a cluster, install PySpark in Colab, and create a Spark session and Spark context to work with SQL and unstructured data, using the Spark UI.

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.

Explore how spark can run on premises or in the cloud, and learn on-prem storage planning with HDFS replication, node sizing, and OS overhead for scalable clusters.

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.

Size a spark cluster by calculating driver and executor ram and cores to process data in parallel, and compare client versus cluster mode under yarn management.

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 to set up a Cloudera quickstart virtual machine to run spark on a single-machine cluster and launch data processing applications via spark submit or spark shell.

Import and start the Cloudera Quickstart VM in Oracle VirtualBox, then SSH into the edge node to access spark shell and spark-submit workflows on the cluster.

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.

Navigate folders to verify spark, pyspark, and python libraries on a cloudera cluster, connect spark shells to hdfs, and use WinSCP to upload files and inspect hive set xml.

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.

Master word count flow with PySpark: load data from HDFS, tokenize lines with flatMap, map words to one, reduce by key, and save the output as text files.

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-submit word count on hdfs with a prepared input file and non-existent output folder, and follow dag, driver, executors, and accumulators during execution.

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.

Persist and unpersist data in Spark by choosing memory or disk storage, using lazy evaluation and actions like collect to optimize Spark workloads.

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.

Learn how accumulators serve as mutable shared variables in Spark core, updating with each RDD element across nodes, and revealing values only when an action triggers computation.

Explore Spark Core as the foundation of Apache Spark, powering the driver and executor, and native functionalities across Spark SQL, streaming, machine learning, and graphics.

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.

Discover Spark SQL use cases across data lakes, data warehousing, and ETL pipelines, handling structured to unstructured data for real-time analytics and machine learning.

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.

Learn spark sql basics, write queries to process data, and understand hive metastore and storage integration, including spark session and contexts guiding data processing.

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.

Explore the Spark SQL official documentation to learn structured data processing with data frames and data sets, including the catalyst optimizer and Hive metastore integration.

Explore Spark SQL dataset concepts, contrast data frames and datasets, and compare runtime versus compile-time schema checks and type safety.

Learn to create a Spark session as the starting point for Spark SQL, configure it with getOrCreate, and load data or create data frames across Scala, Python, Java, and R.

Create data frames by loading data from distributed storage via a spark session using spark.read for json or csv, then display results with df.show.

Explore spark sql df operations by loading data into a data frame, applying transformations such as select, filter, group by, and aggregations, and then create views to run sql queries.

Explore spark sql’s handling of data sources: parquet, json, csv, hive tables, and databases, loading into data frames or tables for sql queries, with performance tuning via catalyst optimizer.

Connect the notebook to the environment, install PySpark, configure a spark session, and load csv or json data into a data frame for display.

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.

Learn to use Spark SQL with temporary views on parquet data, compare dataframe and SQL operations, and explore Hive–Spark integration for max(id) queries and dashboard-ready outputs.

Explore Spark cluster setup with Spark 1.6, including Hive site XML configuration, and transition from RDDs to data frames using Spark Session and SQL context.

Learn how Spark loads data from Hive into Spark, using the Hive metastore to enable data processing while Hive handles warehousing and schema management.

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.

Spark SQL connects to Hive and loads data from Hive tables to analyze the movies and ratings datasets, building dataframe-based analytics.

Load the ratings data into a spark data frame and define a schema with user_id, movie_id, and rating. Use describe to view counts and ranges, preparing for most popular movies.

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.

Compute top rated movies in spark sql by averaging ratings, join with movie details, apply business rules like minimum 500 ratings, and prepare for visualization.

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.

Spark streaming processes real time data from live sources, filtering hashtags and relevant information, saving only what matters for streaming analytics on social media like Twitter.

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.

Create a spark streaming pipeline from a socket source, convert input streams to RDDs, apply transformations like map and filter, and manage the streaming lifecycle with await termination.

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.

Understand the spark structured streaming architecture by wiring read stream and write stream, applying transformations in batches, and maintaining continuous operation with await termination and spark submit.

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.

Explore Databricks as a spark interface and implement spark structured streaming to load json data, compare static and streaming dataframes, and connect to dbfs and clusters.

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.

Trigger a spark streaming cluster example by connecting a socket text stream via netcat, read data in ten-second batches, and print the streamed output to verify data flow.

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 variables and keywords in Python, learn how to assign values with the equals operator, understand data types (integers, floats, strings, booleans), and recognize reserved keywords like def and class.

Explore Python basics: data types, operators, and operands, with hands-on examples of strings, integers, floats, booleans, lists, tuples, dictionaries, and sets, plus type casting and input/output.

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.

Explore Python tuples, compare with lists, and learn how to define, index, slice, and concatenate immutable tuples, including nesting, immutability, and using tuples as dictionary keys.

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 the set data structure in Python, an unordered collection of unique elements defined by curly braces, enabling fast membership tests and operations like union, intersection, difference, and symmetric difference.

Explore Python functions from built-in to user defined, using def and lambda, with examples like even/odd, factorial, and sum of natural numbers, plus default arguments and map, reduce, and filter.

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 loops and iteration, including for and while loops over lists, strings, and dictionaries. Use if/else, range, and list comprehensions for odd/even checks and vouchers.

Learn to open, read, write, append, and close files in Python, manage permissions, and use readline and read methods, with practical examples and guidance toward pandas for data science.

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 object oriented programming in Python, create classes and objects, use constructors, instance and class variables, and apply inheritance, polymorphism, and encapsulation to build simple examples.

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.

Compare file server and client-server architectures, noting file locking, in-memory changes, and how SQL-based updates enable concurrent access with reduced data loss.

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.

Learn constraints in SQL, including not null, unique, and primary key rules. Apply them to create tables and use foreign keys to link related data.

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 how to perform sql joins across multiple tables, including inner, left, right, full outer, and cross joins, using a common column to link data.

Master data import and export in SQL using manual importing, command line, and MySQL Workbench for CSV and Excel, including exporting to SQL files and selective subsets.

Explore aggregate functions in SQL, including group by, count, min, max, and average, with telco churn and gender by contract examples, plus rounding sums.

Explore SQL string functions such as concat, substr, upper and lower, trim, character length, and mid to manipulate and format string data for readable analysis.

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.

Explore views in SQL as virtual tables that display data from other tables and support privacy. Learn to create views to simplify reporting and restrict access without exposing underlying tables.

Explore stored procedures as reusable sql blocks, learn to create, call, and pass in and out parameters with examples like top players, counts, and updates.

Learn to apply sql window functions to compute totals and ranks across related rows, with over, partition by, and order by, using product line and total sales examples.

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.

Explore data engineering fundamentals, from OLTP and OLAP to normalization and denormalization, and the ETL to ELT shift powering data pipelines and data warehouses for analytics.

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 how Hadoop's distributed storage and processing use HDFS blocks and replication across nodes and racks, with MapReduce handling batch data.

Explore distributed storage and processing in big data ecosystems, from Hadoop with MapReduce to Spark and Hive, and learn to orchestrate scalable pipelines in Azure Synapse Analytics and Databricks.

Explore cloud concepts and data formats that power scalable data engineering, from IaaS, PaaS, and SaaS to Parquet, ORC, Avro, Delta, and time travel in modern pipelines.

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 Azure cloud computing fundamentals, including IaaS, PaaS, and SaaS, plus storage, databases, Azure Data Factory, and data engineering services like Synapse and Databricks.

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.

Explore end-to-end data integration and processing on Azure using Azure Data Factory to connect storage (Blob or ADLS), orchestrate pipelines, and copy, transform, and prepare data for analytics.

Learn to build scalable data pipelines with Azure Data Factory, Spark, and Databricks, using data flows, data sets, and medallion architecture (bronze, silver, gold) for efficient processing.

Explore batch and real-time processing in Azure data engineering. Design star and snowflake schemas and manage slowly changing dimensions for analytics.

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

Master azure monitoring and optimization using azure monitor, cost management, and alerts; automate resource decommissioning and align ci/cd pipelines with git, azure devops, adf, and databricks.

Explore Apache Hive architecture, including the Hive metastore, HiveQL, partitioning, and bucketing, and how data storage and processing decouple in cloud big data contexts with Databricks and Azure connections.

Explore how Apache Hive provides warehouse-like capabilities on big data, delivering SQL-like queries over data stored in HDFS via a metastore that maps to table-like structures.

Trace the evolution of Apache Hive, from MapReduce to Tez and Spark, and explore metastore architecture, dag-based execution, and performance boosts with ORC, LLAP, ACID, and Kafka.

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.