Gaurav Dembla

Snowflake Optimization Tip of the Day #5 ➡ Use incremental models in dbt There is no simpler way to reduce Snowflake cost than to process less data. If you are using dbt, try to write every model incrementally. Instead of reprocessing all data every time, incremental models only process new or changed data since the last run. This is achieved primarily by using date stamps or other markers to identify what data has been added or modified since the last run. Full-refreshes are very expensive and should be avoided if possible.

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🚀 Spark Interview Question: What is the difference between managed table & external table. When do you go about creating external tables? 👉 Managed Table: A managed table, also known as an internal table, is a table where the database system controls the lifecycle of the table and its data. This means that when you create a managed table, the database manages the storage of the table's data files, including their location, format, and lifecycle. If you drop a managed table, the database system will also delete the data files associated with that table. 👉 External Table: An external table, on the other hand, is a table where the data files are managed externally, outside the database system. When you create an external table, you specify the location of the data files, and the database system reads the data from those files when querying the table. Unlike managed tables, dropping an external table does not delete the data files; they remain intact in the specified location. 👉 When to Use External Tables: Data Sharing: When you want to share data between different database systems or environments, external tables allow you to reference the same data files from multiple systems without having to duplicate the data. Data Lakes: In data lake architectures, external tables are commonly used to access data stored in formats like Parquet, ORC, or Avro, which are optimized for analytics and are often stored in distributed file systems like Hadoop Distributed File System (HDFS) or Amazon S3. Data Archiving: External tables can be used to store archived data in a cost-effective manner, as the data files can be stored in low-cost storage solutions like Amazon S3 or Azure Blob Storage. Follow me Sampath Vijayan for more insights and tips. #spark #databricks #aws #azure #gcp #datalake #hdfs #s3 #adlsgen2 #gcs

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Day 22: 30-Day Data Engineering Interview Preparation Series Snowflake (#Day22): 20 Snowflake Fundamental interview questions that can be relevant for a data engineering role 👇 📍 What is Snowflake and how is it different from traditional data warehouses? 📍 Can you explain Snowflake's architecture? 📍 What are the key features of Snowflake? 📍 How does Snowflake handle data storage and processing? 📍 What is the concept of virtual warehouses in Snowflake? 📍 How does Snowflake's data sharing work? 📍 Explain the concept of micro-partitions in Snowflake. 📍 How does Snowflake ensure data security and compliance? 📍 What are Snowflake stages and how are they used? 📍 Can you describe the Snowflake query execution process? 📍 What is Time Travel in Snowflake and how does it work? 📍 Explain the concept of zero-copy cloning in Snowflake. 📍 How does Snowflake support semi-structured data? 📍 What are some best practices for optimizing queries in Snowflake? 📍 How does Snowflake's pricing model work? 📍 What are the different types of Snowflake accounts? 📍 How do you handle data loading and unloading in Snowflake? 📍 Can you explain Snowflake's data retention policies? 📍 What is the role of the Information Schema in Snowflake? 📍 How does Snowflake integrate with other data tools and platforms? 🚨 We have kicked off our most affordable and industry-oriented "Complete Data Engineering 3.0 with Azure" Bootcamp. Admissions open! 🔥 👉 Enroll Here: https://lnkd.in/dZSAi93i 🔗Live Classes Starting on 1-June-2024 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary #DataEngineering #SQL #Hive #Hadoop #Kafka #snowflake #Databricks #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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Understanding Narrow and Wide Transformations in Apache Spark In Apache Spark, transformations play a critical role in defining how data is processed across distributed clusters. These transformations can be broadly classified into narrow transformations and wide transformations, each influencing the efficiency and data flow within Spark applications. 1. Narrow Transformations: A narrow transformation is one in which each output partition depends only on a single input partition. Since the data required to generate an output partition resides in the same partition as the input data, Spark can process these transformations without requiring data to move across nodes. This minimizes network traffic and improves performance. Examples of Narrow Transformations: • map(): Applies a function to each element of the RDD/DataFrame without requiring data exchange between partitions. • filter(): Selects elements based on a condition, retaining them within the same partition. • flatMap(): Similar to map(), but can generate multiple elements from a single input element, still preserving the partition boundaries. 2. Wide Transformations: Wide transformations involve operations where the output partition depends on data from multiple input partitions, often triggering a shuffle—a costly operation where data is exchanged across the cluster. These transformations require coordination between nodes, leading to network I/O, which can impact performance. Examples of Wide Transformations: • groupByKey(): Groups data by key, causing a shuffle as all values associated with the same key need to be aggregated across partitions. • reduceByKey(): Similar to groupByKey(), but applies a reducing function during the shuffle phase to optimize data movement. • join(): Combines data from two datasets based on a key, triggering a shuffle to align matching keys across partitions. Key Difference: Data Shuffling The fundamental difference between narrow and wide transformations lies in data shuffling. In narrow transformations, Spark processes data locally without any network overhead. However, in wide transformations, the data must be reshuffled across partitions, leading to increased computational and network costs. For example, a map() operation applies functions independently within partitions, while a reduceByKey() requires the data to be shuffled so that values for the same key are processed together. Follow me(Raghu Raman A V) for more articles on Big Data, Data Engineering, Azure and cloud ! #BigData #ApacheSpark #DataEngineering

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Someone rightly said : Snowflake is for rich, low to no-tech departments like Finance. Databricks is for data engineers. In the end, both are successful, multi-billion dollar companies. Choice in the end lies in defining a clear customer segment. #snowflake #databricks #dataengineering #customersegment

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Top 10 Snowflake Interview Questions: 1. What is Snowflake and why is it important? - Focus on Snowflake's cloud-native design, separation of compute and storage, and ability to handle structured and semi-structured data. 2. What are the different Snowflake editions, and which one should you choose? - Discuss Standard, Enterprise, Business Critical, and Virtual Private Snowflake, and highlight use cases for each edition. 3. How does Snowflake's architecture work? - Explain the separation of compute, storage, and cloud services, and how it supports concurrency and dynamic scaling. 4. How do you set up a Snowflake account? - Outline the steps, including account creation, role-based access control (RBAC), and enabling multi-factor authentication (MFA). 5. How does role-based access control (RBAC) work in Snowflake? - Describe the creation and management of roles, and how RBAC simplifies security and permissions management. 6. What types of tables are available in Snowflake? - Discuss Permanent, Temporary, and Transient tables, and their use cases for storage and query performance. 7. How do you use standard and materialized views in Snowflake? - Highlight the differences and benefits of real-time querying with standard views versus performance optimization with materialized views. 8. What are Snowflake stages and how do you manage them? - Differentiate between internal and external stages and explain their role in data loading workflows. 9. What are the different data loading approaches in Snowflake? - Explain bulk loading and continuous loading (Snowpipe), and the scenarios where each approach is suitable. 10. What is Snowflake’s Time Travel and Fail Safe feature? - Detail Time Travel for historical data queries and Fail Safe for recovering permanently deleted data. #Snowflake #snowflakeinterviewquestions #dataengineering

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Day 12 - 30-Day Data Engineering Interview Preparation Series PySpark (#Day12): 20 PySpark interview questions that can be relevant for a data engineering role 👇 📍 What is PySpark, and how does it differ from Apache Spark? 📍 Explain the architecture of Spark. 📍 How do you initialize a SparkSession in PySpark? 📍 What are RDDs in PySpark, and how do they work? 📍 What are the transformations and actions available in RDDs? 📍 How do you create RDDs in PySpark? 📍 What are DataFrames in PySpark, and how do they differ from RDDs? 📍 How do you perform operations on DataFrames? 📍 Explain the concept of a Dataset in PySpark. 📍 How do you work with SparkSQL in PySpark? 📍 Explain how to perform SQL operations on DataFrames. 📍 What are the advantages of using SparkSQL? 📍 How do you read and write data in various formats (CSV, JSON, Parquet) using PySpark? 📍 Explain the process of handling schema evolution in PySpark. 📍 What is Catalyst Optimizer in PySpark? 📍 How do you optimize a PySpark application? 📍 Explain the concept of lazy evaluation in PySpark. 📍 What is the significance of caching and persistence in PySpark? 📍 How do you handle skewed data in PySpark? 📍 What are broadcast variables and accumulators in PySpark? 🚨 We have launched our most affordable and industry-oriented "Complete Data Engineering 3.0 With Azure" bootcamp and ADMISSIONS ARE OPEN! 🔥 Only 7 days left and 80 seats remaining! 🚀 Enrollment link is in the first comment 👇 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary Shubhankit Sirvaiya Aman Kumar 🙂 #DataEngineering #SQL #Hive #Hadoop #Kafka #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure

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**𝐋𝐞𝐯𝐞𝐥 𝐔𝐩 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐤𝐢𝐥𝐥𝐬 𝐰𝐢𝐭𝐡 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠!** A huge shoutout to my friends, Vishal Kaushal , a Snowflake Squad member, and Sachin Mittal, a 4x Snowflake Data Superhero, who are starting Snowflake Training: Zero to Hero course starting May 18th! . **Why Snowflake? ** Snowflake is more than just a data warehouse. It's a powerful, cloud-native data platform that empowers data engineers to build next-generation data solutions. Here's why learning Snowflake can be a game-changer for your career. By mastering Snowflake, you'll equip yourself with the skills to design and implement robust, scalable data solutions that are in high demand across industries. This positions you as a valuable asset in the ever-evolving world of data engineering. This comprehensive training, will equip you with the knowledge and skills to become a Snowflake master. Here's what you'll gain: Solid Foundation: Grasp the core concepts and functionalities of Snowflake. Hands-on Learning: Get practical experience through real-world exercises and projects. Unlimited doubt support Real time Projects : 2 End to End Real time projects , 1 using Matillion as well. Ready to take your data engineering skills to the next level? Don't miss out on this amazing opportunity! Sign up for the Snowflake Training: Zero to Hero course starting May 18th. 👉🏻 Enroll Here - https://lnkd.in/gcfxzNx4 📅 Live classes starts from 18th May 2024 Discount Coupon : LIVE20 PS : This is not a paid post. #Snowflake #DataEngineering #CloudDataWarehouse #Training #Upskilling #DataSuperhero

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Recently Asked Data Engineering Interview Questions 👇 🔹 Given a table of sales transactions, write a query to calculate the cumulative sales for each product over time. 🔹 How would you handle NULL values in SQL when performing aggregations? 🔹 Explain the concept of data partitioning and its significance in distributed systems. 🔹 How does Apache Spark handle data processing, and what are its advantages over Hadoop MapReduce? 🔹 Explain the role of HDFS in the Hadoop ecosystem. 🔹 What is the purpose of a message broker like Kafka in a data pipeline? 🔹 Write a Python script to merge two sorted lists into a single sorted list. 🔹 How would you handle large datasets in Python to ensure efficient memory usage? 🔹 What are normalization and denormalization in database design? Provide examples of when to use each. 🔹 Explain the concept of serverless computing and its advantages. 🔹 How would you deploy a data pipeline in a cloud environment? 🔹 What is the Data Catalog in AWS Glue? 🔹 Difference between Athena and Aurora. 🔹 What are the key components of a data governance framework? 🔹 Explain the importance of data lineage and how it can be tracked. 🔹 Explain the CAP theorem and its implications for distributed databases. 🚨 We have started the batch of "Data Engineering With AWS" BootCAMP which is high quality, affordable, practical & industry grade project oriented✌🏻We have included Apache Flink, Hudi & Iceberg too😇 👉 Enroll Here - https://bit.ly/3Y5gCJE 🎉 Dedicated placement assistance & doubt support 📲 Call/WhatsApp for any query (+91) 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 😎 

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🚀 Ready to master Azure Databricks? 🚀 Learn how to create a cluster in Azure Databricks with all the essential configurations and best practices! Whether you're just starting or looking to sharpen your skills, this video will guide you through the process step by step. 👉 Watch the tutorial here: https://lnkd.in/dftZR7kU #Azure #Databricks #BigData #CloudComputing #DataScience #MachineLearning #TechTutorials #AzureDatabricks

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Day 15 - 30-Day Data Engineering Interview Preparation Series Databricks (#Day15): 20 Databricks Fundamentals interview questions that can be relevant for a data engineering role 👇 📍 What is Databricks, and how does it differ from Apache Spark? 📍 Explain the key features of Databricks. 📍 What are the main components of Databricks architecture? 📍 How do you set up a Databricks workspace? 📍 Describe the process of creating a cluster in Databricks. 📍 What are the different cluster modes available in Databricks? 📍 How do you import data into Databricks? 📍 What are the supported file formats for data import/export in Databricks? 📍 How do you export data from Databricks? 📍 Explain the concept of Delta Lake in Databricks. 📍 What are Delta tables, and why are they useful? 📍 How do you create a Delta table in Databricks? 📍 What are the different operations you can perform on Delta tables? 📍 How do you manage and optimize Delta tables? 📍 Explain the role of PySpark notebooks in Databricks. 📍 How do you create and run a PySpark notebook in Databricks? 📍 What are the common PySpark operations you perform in Databricks? 📍 How do you execute PySpark code on a Databricks cluster? 📍 Explain the concept of workflows in Databricks. 📍 How do you create and manage workflows in Databricks? 🚨 Last 4 days left to join my most affordable and industry oriented "Complete Data Engineering 3.0 With Azure" Bootcamp, ADMISSIONS ARE OPEN for limited seats🔥 👉 Enroll Here (Last Few Seats): https://lnkd.in/dZSAi93i 🔗 Code "DE300" for my Linkedin connections 🚀 Live Classes Starting on 1-June-2024 📲 Call/WhatsApp on this number for career counselling and any query +91 9893181542 Cheers - Grow Data Skills Shashank Mishra 🇮🇳 SHAILJA MISHRA🟢 Sahil Choudhary 😎 #DataEngineering #SQL #Hive #Hadoop #Kafka #Databricks #Pyspark #AdvancedSQL #DataScience #BigData #TechInterview #InterviewPreparation #CareerGrowth #TechSkills #SQLQueries #azure #30daysinterviewprep

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Snowflake Interview Question!! Q1- Can you explain how Snowflake’s architecture, with its separation of storage and compute, automatic micro-partitioning, and result caching, helps in optimizing query performance? Snowflake’s architecture optimizes query performance through several key features: 1.Separation of Storage and Compute: This allows compute resources to be scaled independently of storage. It enables allocating more compute power during peak query times without impacting data storage costs. Concurrent workloads are supported by allowing multiple virtual warehouses to access the same data simultaneously, ensuring high performance and resource isolation. 2.Automatic Micro-Partitioning: Snowflake stores data in small, contiguous units known as micro-partitions. These are automatically managed and optimized by Snowflake, including creating, reorganizing, and optimizing the partitions while ingesting and querying data. Extensive metadata for each micro-partition efficiently prunes irrelevant data during query execution, reducing the scanned data volume and enhancing query performance. 3.Result Caching: Snowflake caches results of previously executed queries. When the same query is rerun, Snowflake retrieves results from the cache instead of rerunning the query, significantly reducing execution time and resource usage. This automatic result caching accelerates repeated queries and provides cost savings by minimizing the need for additional compute resources. Q2- How clustering keys can optimize data retrieval and reduce scan times.  Clustering keys in Snowflake optimize data retrieval and reduce scan times by organizing data within micro-partitions based on specified columns. This helps improve query performance, especially for large datasets. Benefits of Clustering Keys 1.Reduced Scan Times: By clustering data, Snowflake can skip irrelevant micro-partitions during query execution, leading to faster access times. 2.Efficient Filtering: Clustering helps the engine locate relevant rows quickly when queries filter on clustered columns, minimizing the amount of data scanned. 3.Data Pruning: Snowflake uses clustering keys to prune entire partitions that do not meet query criteria, significantly improving query performance. #snowflake #bigdata #dataengineer #pyspark

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Use Cases and Best Practices Data quality is the backbone of reliable analytics, especially in big data environments. I recently explored methods to manage null values and outliers, which can significantly impact results if not addressed. Using PySpark’s.fillna() and .dropna() functions has been useful for scalable transformations. What are some of your go-to strategies for ensuring high data quality? #dataengineering #seniormanagers #dataarchitect #bigdata #databricks #dataquality #datacleaning #dataprofiling #datavalidation

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I like Databricks' dynamic resource allocation; I think it is great. However, I would be cautious when a developer sets the maximum worker nodes to 20. You risk wasting resources and spending a lot of money because the allocation and deallocation of resources are not simple processes behind the scenes. Often, it takes a while to deallocate the newly added executors, leading to overpayment for low utilization. A quick look at the Metrics UI can provide insight into this issue. Instead, try to resize your cluster compute to a larger size, ensuring you do not exceed a maximum of 8 worker nodes in your upper limit setting. #databricks #dataengineer

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Power BI On Demand (in Hindi) May 2024 Update: New Matrix Layouts | Repeating Row Headers: https://lnkd.in/gVgDYbh6 #PowerBI #May2024 #DAX #MatrixLayouts #DataAnalysis #RepeatingRowHeaders #BusinessIntelligence #PowerBITutorial #Analytics

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Power BI On Demand (in Hindi) May 2024 Update: New Matrix Layouts | Repeating Row Headers: https://lnkd.in/gVgDYbh6 #PowerBI #May2024 #DAX #MatrixLayouts #DataAnalysis #RepeatingRowHeaders #BusinessIntelligence #PowerBITutorial #Analytics

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What is SparkSession and Sparkcontext? SparkContext:- Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. Syntax for SparkContext: from pyspark import SparkContext --Create a SparkContext object with local mode and specifying the number of worker threads sc = SparkContext("local[*]", "example") --Now you can use 'sc' to create RDDs and perform operations on them SparkSession:- It’s a unified object to perform all the Spark operations. In the earlier version of the Spark 1.x there were separate objects like SparkContext, SQLContext, HiveContext, SparkConf, and StreamingContext. However with Spark 2.x all these different objects combine into one i.e. the SparkSession. You can perform all those operations using the SparkSession object itself. This unison of all the objects has made life simpler for the Spark Developers. Syntax for SparkSession from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .appName("Name") \ .getOrCreate() Why should use SparkSession over SparkContext? from Spark 2.0, SparkSession provides a common entry point for a Spark application. Instead of SparkContext, HiveContext, SQLContext, everything is now within a SparkSession. it unifies all of sparks numerous contexts. before version 2.0 need to create separate context per JVM. however with SparkSession this problem has been resolved. Check out this databricks interview questions course: https://lnkd.in/dCjUuEGe

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Databricks AI Security Framework (DASF) In the evolving landscape of AI and machine learning, security and privacy are paramount. The Databricks AI Security Framework (DASF) provides a comprehensive strategy to address these critical concerns. Key highlights from the DASF whitepaper include: 1. 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐑𝐢𝐬𝐤 𝐌𝐢𝐭𝐢𝐠𝐚𝐭𝐢𝐨𝐧: Covers 12 foundational components of AI systems, including raw data, data preparation, datasets, model management, and more. 2. 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐃𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐬: Offers specific recommendations to safeguard AI systems against potential threats. 3. 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐀𝐜𝐫𝐨𝐬𝐬 𝐓𝐞𝐚𝐦𝐬: Designed to foster structured conversations between business, IT, data, AI, and security teams. 4. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Aligns with key regulatory frameworks, including President Biden’s Executive Order on AI and NIST’s AI Risk Management Framework. Main Risks Identified: • 𝐈𝐧𝐬𝐮𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐚𝐜𝐜𝐞𝐬𝐬 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬 • 𝐏𝐨𝐨𝐫 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 • 𝐃𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠 • 𝐌𝐨𝐝𝐞𝐥 𝐝𝐫𝐢𝐟𝐭 • 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐬𝐭𝐞𝐚𝐥𝐢𝐧𝐠 • 𝐌𝐚𝐥𝐢𝐜𝐢𝐨𝐮𝐬 𝐥𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬

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The estimated total pay for a Big Data Architect is $203,441 per year, with an average salary of $145,928 per year. Courses to Get Started ⤵️ ⤵️ ⤵️ Big Data Specialization (Coursera) Building a comprehensive understanding of the Big Data ecosystem. 🔗 https://lnkd.in/g95iHeW9 Apache Spark for Data Engineering" by IBM (In-depth Spark course) 🔗 https://edx.sjv.io/bO3Qbv "Data Engineering with Google Cloud professional certification" by Google Cloud (Covers BigQuery, Dataflow, Dataproc) 🔗 https://lnkd.in/dcPPMQ5z "DP-203: Data Engineering on Microsoft Azure" (Prepares for the Azure Data Engineer Associate certification) 🔗 https://lnkd.in/gCrBwDJB A Detailed RoadMap to Become A Big Data Engineer/Architect ⤵️ Phase 1️⃣ : Foundation (6-12 months) ⤵️ - Programming: Python (NumPy, Pandas, Dask, FastAPI), Java (JVM, concurrency, Spring Boot), Scala (optional). - Databases: SQL (advanced queries, optimization), NoSQL (MongoDB, Cassandra, Redis, Neo4j). - Linux: Command line, shell scripting, system administration basics. Data Warehousing/ETL: Dimensional modeling, ETL tools (AWS Glue, Azure Data Factory, Google Cloud Dataflow). Phase 2️⃣ : Big Data Ecosystem (12-18 months) ⤵️ - Hadoop: HDFS, YARN, MapReduce, Hive (HiveQL, optimization), HBase. - Spark: Spark Core (RDDs, PySpark), Spark SQL, Spark Streaming, MLlib. Phase 3️⃣ : Expanding the Toolkit (12-18 months) ⤵️ - Real-time Streaming: Kafka (Connect, Streams, Schema Registry), Pulsar, Kinesis, Azure Stream Analytics. - Workflow Orchestration: Airflow, Prefect, Dagster. - Advanced Processing: Flink (SQL, stream processing), Presto. - NoSQL (Advanced): Data modeling patterns, consistency/availability, administration. Phase 4️⃣ : Cloud and Modern Architectures (12-18 months) ⤵️ - Cloud Platforms: AWS (EMR, Redshift, Kinesis, Glue), Azure (HDInsight, Synapse, Databricks), GCP (Dataproc, BigQuery, Dataflow). - Data Architectures: Data lakes, data lakehouse (Delta Lake), data mesh. - Serverless: AWS Lambda, Azure Functions, Google Cloud Functions. Phase 5️⃣ : Real-World Projects (Ongoing) ⤵️ - Data Modeling (Advanced): Dimensional modeling, data vault, schema design. - Data Quality: Fundamentals, tools, and techniques. - Data Governance: Principles, security, and privacy. - Performance Optimization: Query and system tuning. - Project Ideas: Beginner: Blog data pipeline, website traffic analysis, simple recommender. - Intermediate: Real-time fraud detection, e-commerce data lake, social media data warehouse. - Advanced: Scalable IoT data platform, image recognition pipeline, enterprise data mesh.

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