Nitin Bobde

Choosing the Right Data Warehouse Architecture: Inmon vs. Kimball Selecting the best data warehouse architecture is crucial for your organization’s data management. Let’s dive into two popular approaches—Inmon’s and Kimball’s—and see which one might be the best fit for you. 🏛️ Inmon’s Approach (Top-Down) 🔹 Overview: Inmon’s approach emphasizes building a centralized, normalized data warehouse that excels in handling complex queries and ensuring long-term data integrity. 🔹 Key Steps: Extract data from various sources. Load it into a staging area. Transform and load into a normalized Data Warehouse (3NF). Create Data Marts tailored for specific business needs. 🔹 Structure: Centralized Data Warehouse 🔹 Advantages: Ideal for complex queries and maintaining data integrity over time. 🔹 Considerations: The initial setup can be complex and time-consuming, delaying immediate benefits. 🚀 Kimball’s Approach (Bottom-Up) 🔹 Overview: Kimball’s approach focuses on speed and ease of use by creating decentralized data marts that come together to form a data warehouse. 🔹 Key Steps: Extract data from operational sources. Load it into a staging area. Transform and load directly into Data Marts. Integrate these Data Marts to build a Data Warehouse (Star/Snowflake Schema). 🔹 Structure: Decentralized Data Marts 🔹 Advantages: Faster implementation, offering quick insights, and user-friendly for business teams. 🔹 Considerations: Potential for data redundancy and integration challenges in the long run. CC: Deepak #DataWarehouse #InmonVsKimball #DataArchitecture #BusinessIntelligence #DataStrategy #DataManagement #BigData #DataEngineering #AI #Analytics #TechTrend

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Spark Query to create a new column based on age column in the table. #BIGDATA #SCALA #SPARK Karthik K. def main(args:Array[String]):Unit={ val ss = SparkSession.builder.appName("WHEN_OTHERWISE").master("local[*]").getOrCreate() import ss.implicits._ val data = List(("M",17),("F",25),("M",30),("F",35),("M",10)).toDF("Gender","Age") val df_with_age_remark = data.select(col("Gender"),col("Age"),when(col("Age")>=18,true).otherwise(false).as("IS ADULT")) println("Printing Age Remarks") df_with_age_remark.show() OUTPUT:

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SPARK code to impute NULL or missing values in a table containing NULL in multiple columns. #SPARK #SCALA #BIGDATA Karthik K. def main(args:Array[String]):Unit = { val ss = SparkSession.builder.master("local[*]").appName("DFDEMO").getOrCreate() import ss.implicits._ val df_1 = ss.read.format("csv").option("header",true). option("path","C:/Users/hp/COMPANY.txt").load() df_1.show() val df_2 = df_1.select(col("PRODUCT_NAME"),col("PRODUCT_ID") , (when(col("PRODUCT_ID").isNull,0).otherwise(col("PRODUCT_ID")).as("LEGIT_PRODUCT_ID")), when(col("PRODUCT_NAME").isNull,"UNKNOWN COMPANY"). otherwise(col("PRODUCT_NAME")).as("LEGIT_PRODUCT_NAME")) df_2.show() OUTPUT:

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TOP FIVE REASON TO CHOOSE Snowflake 1- ONE SOLUTION FOR ALL DATA RELATED PROBLEMS 2- LIMITLESS SCALIBILITY 3- SQL SUPPORT & EASY TO LEARN 4- CROSS CLOUD & CROSS REGION CONNECTIVITY SUPPORT 5- NEAR ZERO MAINTAINACE ACTIVITY REQUIRED Snowflake is one of the popular skill for Data engineer these days so learning Snowflake will help you to shape your Data engineering career. Me and Sachin Mittal are starting our next Snowflake bootcamp from 18th MAY,which will focus all the key capabilities of Snowflake , Data Integration, Sharing ,designing DWH ,Dashboard and Snowpark. 𝐊𝐄𝐘 𝐇𝐈𝐆𝐇𝐋𝐈𝐆𝐇𝐓𝐒 👇🏻    ✅ Course Prerequisite : SQL knowledge is mandatory.  ✅ Comprehensive Curriculum: From Basic to Advanced levels, Covering all the key points required to become Snowflake Data engineer. ✅ 25+ Live Classes: It will be all Live and Interactive session with Practical, hands-on sessions with proper Assignments/ ✅ Unlimited Doubt Support : Lifetime Recordings & Unlimited Doubt even after Course completion.  ✅ Interview & Certification : Certification sample questions , Quizzes and Interview questions & answers. ✅ End to End Project : End to End Data engineering project using Matillion,Snowflake & Power BI. 👉🏻 Enroll Here - https://lnkd.in/gcfxzNx4 📅 Live classes starts from 18th May 2024 Discount Coupon : LIVE20 REGISTER SOON. Feel free to ping me for any query. #snowflake #cloudengineering #dataengineering #matillion #dataengineer #analyticsengineer

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#What is ForEach Activity in Azure Data Factory? The ForEach Activity defines a repeating control flow in an Azure Data Factory or Synapse pipeline. This activity is used to iterate over a collection and executes specified activities in a loop. The loop implementation of this activity is similar to Foreach looping structure in programming languages. #dataanalyst #dataengineering #Azure #ADF

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How to Import Data Files from a Folder in Power BI https://lnkd.in/d6bser5S #businessintelligence #powerbi #datascience #dataanalysis #datavisualization #analytics #dataanalytics #azure #sql #technology #future #futurism #softwareengineering #programing #softwaredesign #datamining #data #computerscience #python #fintech #cloud #automation #productmanagement #ai #userexperience #cloudcomputing #mobileapplications #bigdata

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Spark Lazy Evaluation!! Certainly! Lazy evaluation in Apache Spark is a concept where Spark delays the execution of transformations until it's absolutely necessary. Instead of processing data immediately when a transformation is called, Spark builds up a directed acyclic graph (DAG) of the transformations and waits until an action is invoked to trigger the actual computation. Interviewer: Let's start with the basics. Can you explain what lazy evaluation means in the context of Apache Spark? Candidate: Sure. Lazy evaluation means that Spark doesn't execute transformations right away when they're called. Instead, it builds up a logical execution plan, known as a DAG, to represent the sequence of transformations. The actual execution doesn't occur until an action is invoked on the RDD. Interviewer: Excellent explanation. Now, why is lazy evaluation important in Spark? Candidate: Lazy evaluation offers several advantages. Firstly, it helps to optimize performance by allowing Spark to optimize the execution plan before actually executing it. This optimization can include fusion of operations, predicate pushdown, and other optimizations that improve efficiency. Interviewer: That's spot on. Lazy evaluation indeed enables Spark to optimize the execution plan based on the entire DAG rather than just individual transformations. Can you elaborate on any other benefits of lazy evaluation? Candidate: Certainly. Lazy evaluation also improves fault tolerance. Since Spark maintains the lineage information of RDDs, if a partition of data is lost due to a node failure, Spark can efficiently recover it by recomputing only the lost partition rather than rerunning the entire transformation chain. Interviewer: Good point. Lazy evaluation coupled with RDD lineage indeed enhances fault tolerance, making Spark resilient to failures. Now, let's talk about any potential drawbacks or challenges associated with lazy evaluation. Candidate: One challenge is debugging. Since transformations are not executed immediately, errors may not surface until an action is triggered. This can make it more challenging to pinpoint the source of errors in the code. Interviewer: Absolutely. Debugging can indeed be more challenging with lazy evaluation, especially when dealing with complex transformations. However, proper logging and debugging techniques can mitigate this challenge to a certain extent. Candidate: Agreed. Despite the challenges, the benefits of lazy evaluation in terms of performance optimization and fault tolerance make it a crucial aspect of Apache Spark's architecture. Interviewer: Well said. Lazy evaluation is indeed a fundamental concept in Spark, enabling efficient distributed data processing while ensuring fault tolerance and optimization. Thank you for the insightful discussion. Candidate: Thank you for the opportunity to discuss this topic. It was a pleasure. #spark #bigdata

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🚀 FinOps Tagging Coverage KPI Power BI Dashboard in 5 minutes! This tutorial provides the skills and insights you need to excel in FinOps visualization. 1️⃣ Watch the Tutorial 2️⃣ Get the Complete Toolkit 3️⃣ Share this tutorial Want to follow along and create your own dashboard with ease? The toolkit includes: ✅ Power BI - PBIX File ✅ DAX Queries ✅ Models ✅ Sample Billing Data (AWS, Azure, and GCP) 🛒 Get Instant Access to the Toolkit Here https://lnkd.in/gBMzsx7h Don't miss this opportunity to enhance your skills and apply practical insights to real-world scenarios. Feel free to share your thoughts, questions, or feedback in the comments below. Let's connect and grow together in the world of FinOps visualization! #finops #powerbi #kpi #finance #tagging #cloudengineering

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Understanding Parallelism Wait Statistics in SQL Server

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The Top 18 Most Common Issues when Troubleshooting the Performance of a Data Model in Power BI ➡️ Non Optimised Data Loads ➡️ Slow Report Interactions ➡️ Complex Calculations Affecting Performance ➡️ High Memory Usage ➡️ Inaccurate Data Display ➡️ Inconsistent Row-Level Security ➡️ Excessive Visual Load Times ➡️ High Cardinality Slowing Model ➡️ Frequent Full Refreshes Overloading System ➡️ Excessive Measures ➡️ Managing Data Security Across Roles ➡️ Inefficient Use of Hierarchies ➡️ Calculations Not Matching Business Logic ➡️ Difficulty Understanding DAX Formulas ➡️ Need for Aggregated Data for High-Level Views ➡️ Trouble Identifying Table Purpose or Structure ➡️ Unexpected Relationships or Data Linkage Errors #PowerBI

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١ تعليق واحد

Microsoft Fabric in Hindi- Notebooks: Essential PySpark Functions for Analysis| Describe, join, groupBy :https://lnkd.in/deEB63g9 #microsoftfabric #Notebooks #pySpark #dataPipeline #Pipeline #powerbi #Warehouse #Dataflow #DataflowGen2 #DataFactory #OneLake #Intergated #analytics

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Myths of Data Governance & Security : Data governance and security are important priorities. Even with high-performance data pipelines, rapid data transformation, and superior data processing tools, their value decreases if data is not properly stored and controlled, data stored and accessed according to local regulations meet is important, and is organized covered to ensure identifiable and a valuable to the organization.

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Power BI, Relational and Object Storage What's the difference and when to use which storage... Here's a breakdown of the key differences between relational storage and object storage, along with their roles in current data architectures: Relational Storage: Structure: Data is organized in tables with rows and columns, similar to a spreadsheet. Relationships: Tables are linked based on common fields, allowing for efficient retrieval of related data. This is ideal for structured, transactional data with well-defined relationships. SQL Queries: Uses SQL, a structured query language, for data retrieval and manipulation. Examples: Traditional databases like MySQL, Oracle, Microsoft SQL Server. Object Storage: Flexibility: Data is stored as self-contained objects with unique identifiers and associated metadata (descriptive information). Objects can be structured, semi-structured, or unstructured. Scalability: Highly scalable for storing vast amounts of diverse data, making it well-suited for data lakes and big data environments. Simple Access: Objects are accessed directly using their unique identifiers, without complex relationships between them. Examples: Amazon S3, Azure Blob Storage, Google Cloud Storage. Dominance in Data Architectures: In today's data landscape, both relational and object storage play crucial roles: Relational Storage: Still dominates for mission-critical applications and analytical workloads that rely on structured, well-defined data with strong relationships. It excels in transactions, ensuring data consistency and integrity. Object Storage: Gaining prominence due to the growing volume of unstructured and semi-structured data. It's ideal for data lakes, archives, and storing various data types like images, videos, and sensor data. Its scalability and cost-effectiveness make it attractive for big data solutions. Modern Data Architectures: Many organizations adopt a hybrid approach, leveraging both relational and object storage for different purposes within their data landscape. Here's a common scenario: Data Lake (Object Storage): Stores all incoming data, regardless of format, in its native state. Data Warehouse (Relational Storage): Curated, structured data extracted and transformed from the data lake is loaded into a relational database for efficient analysis using SQL. Choosing the Right Storage: The choice between relational and object storage depends on your specific data needs: Structured Data with Relationships: Relational storage is ideal. Large-scale, Diverse Data: Object storage is a good fit. Hybrid Scenarios: Combine both for a comprehensive data management strategy. By understanding the strengths of each storage type, you can create an effective data architecture that meets your organization's evolving data management requirements. #LifelongLearning #OnlineEducation #DataScience #ContinuousImprovement #SkillDevelopment #PowerBI #Oman #Muscat #GCC #Training #Consulting

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Microsoft Fabric Hindi: Time travel in Microsoft Fabric Warehouse| Historical Data with Time Travel: https://lnkd.in/gEQMHhua #PowerBI #dax #powerquery #AmitChandakDA #businessintelligence #PL300 #powerplatform #bi #powerbidesktop #datatodiscovery #powerbitutorial

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١ تعليق واحد

🚀 FinOps Tagging Coverage KPI Power BI Dashboard in 5 minutes! This tutorial provides the skills and insights you need to excel in FinOps visualization. 1️⃣ Watch the Tutorial 2️⃣ Get the Complete Toolkit 3️⃣ Share this tutorial Want to follow along and create your own dashboard with ease? The toolkit includes: ✅ Power BI - PBIX File ✅ DAX Queries ✅ Models ✅ Sample Billing Data (AWS, Azure, and GCP) 🛒 Get Instant Access to the Toolkit Here https://vist.ly/3mktyyx #finops #powerbi #kpi #finance #tagging #cloudengineering

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