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OFFSET In Spark Sql You might be in a kind of situtaion where, you wanna skip some initial rows from a final obtained result? i.e not selecting TOP n rows instead starting from nth row to get the desired result. With release of spark 3.5.1 you get a clause with which its easily achievable. OFFSET clause is used to specify the number of rows to skip before beginning to return rows returned by the SELECT statement. This clause is used in conjunction with ORDER BY or LIMIT to ensure that the results are deterministic. 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍 𝑬𝒗𝒆𝒓𝒚𝒅𝒂𝒚, 𝑰 𝒍𝒆𝒂𝒓𝒏 𝒂𝒏𝒅 𝒔𝒉𝒂𝒓𝒆 𝒔𝒕𝒖𝒇𝒇 𝒂𝒃𝒐𝒖𝒕: 🌀 Data Engineering 🌀 Python/SQL 🌀 Databricks/Pyspark 🌀 Azure 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dGDBXWRY 👉𝐅𝐨𝐥𝐥𝐨𝐰 Riya Khandelwal 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭. #DataEngineeringInterview #ApacheSpark #InterviewPrep #DataEngineering #TechInterviewTips #data #dataengineering  #dataengineerjobs #interviewquestions #interviewprep #newlearnings #interviewpreparation #interviewtips  #dataengineers #interviewsuccess  #followme  #ETL  #dataengineerjobs #spark  #azurecloud #datacleaning #pyspark  #optimization #interviewsuccess #interviewskills  #DataEngineering #DataScience  #LinkedInLearning #DataProcessing #BigData  #InterviewPrep #TechCareer #DataJobs #KnowledgeSharing #TechSkills #BigData #LearningResources #LinkedInLearning #DataScience #PySparkFunctions #CareerDevelopment #mapreduce #interviewguidance #interviewingskills #PySpark #DataCleaning #DataScience #LinkedInLearning #certified #certification #azurecertified #codingchallenge #hadoop #hadoopadministration #hadoopdeveloper #DeltaLake #DataLakes #BigData #DataEngineering #DataScience #DataManagement #LinkedInLearning #DataInfrastructure

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OFFSET In Spark Sql You might be in a kind of situtaion where, you wanna skip some initial rows from a final obtained result? i.e not selecting TOP n rows instead starting from nth row to get the desired result. With release of spark 3.5.1 you get a clause with which its easily achievable. OFFSET clause is used to specify the number of rows to skip before beginning to return rows returned by the SELECT statement. This clause is used in conjunction with ORDER BY or LIMIT to ensure that the results are deterministic. 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍 𝑬𝒗𝒆𝒓𝒚𝒅𝒂𝒚, 𝑰 𝒍𝒆𝒂𝒓𝒏 𝒂𝒏𝒅 𝒔𝒉𝒂𝒓𝒆 𝒔𝒕𝒖𝒇𝒇 𝒂𝒃𝒐𝒖𝒕: 🌀 Data Engineering 🌀 Python/SQL 🌀 Databricks/Pyspark 🌀 Azure 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/dGDBXWRY 👉𝐅𝐨𝐥𝐥𝐨𝐰 Riya Khandelwal 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭. #DataEngineeringInterview #ApacheSpark #InterviewPrep #DataEngineering #TechInterviewTips #data #dataengineering  #dataengineerjobs #interviewquestions #interviewprep #newlearnings #interviewpreparation #interviewtips  #dataengineers #interviewsuccess  #followme  #ETL  #dataengineerjobs #spark  #azurecloud #datacleaning #pyspark  #optimization #interviewsuccess #interviewskills  #DataEngineering #DataScience  #LinkedInLearning #DataProcessing #BigData  #InterviewPrep #TechCareer #DataJobs #KnowledgeSharing #TechSkills #BigData #LearningResources #LinkedInLearning #DataScience #PySparkFunctions #CareerDevelopment #mapreduce #interviewguidance #interviewingskills #PySpark #DataCleaning #DataScience #LinkedInLearning #certified #certification #azurecertified #codingchallenge #hadoop #hadoopadministration #hadoopdeveloper #DeltaLake #DataLakes #BigData #DataEngineering #DataScience #DataManagement #LinkedInLearning #DataInfrastructure

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🌟 Exciting News for Snowflake Enthusiasts in Hyderabad! 🌟 The #Snowflake Hyderabad User Group is back with another amazing in-person meetup, hosted by Snowflake. Join us on December 07, 2024, from 9:00 AM to 1:00 PM, for a hands-on workshop led by Satish T, Cloud Data Architect at Infocepts Pvt Ltd and Snowflake Data SuperHero. In this workshop, you’ll learn how to: 🔍 Translate multilingual customer reviews 📊 Categorize and analyze unstructured review data at scale ⭐ Extract ratings and sentiments from reviews 💡 Identify customer issues and take action with LLM assistance 💻 What You’ll Build: We’ll analyze Tasty Bytes customer reviews using Snowflake Cortex within Snowflake Notebooks to discover: ✅ How likely customers are to recommend Tasty Bytes ✅ Sentiments around food, pricing, and other aspects ✅ Key issues customers highlight and how to address them 🎯 Prerequisites: Familiarity with Python and DataFrame API Experience with Snowflake and Snowpark A Snowflake account with access to Cortex LLM functions/models and Snowflake Notebooks. Don’t miss this opportunity to level up your skills with Snowflake Cortex. #Snowflake #LLM #dataengineer #Cortex #dbt #genai

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****CASE STUDY- Evaluating the ability of DBT (Data Build Tool) to support platform-independent BI & MI reporting Solutions**** A few days back I went through some introductory training on DBT (Data Build Tool) and it inspired me to run a simple case study. DBT is a wrapper or an abstraction on SQL, which brings out an interesting advantage. A lot of the BI (Business Intelligence) and MI (Management Information) reporting solutions or data analytics solutions that we build on different data platforms can often become very tightly coupled with the underlying platform which could be a data warehouse, a data lake or a data lakehouse. How then, can you reduce the cost of migrating from a legacy platform to a more modern platform? Modernization and migration is a big part of the data industry. It simply cannot be avoided. So, is there way that we can decouple our BI and MI solutions or our data analytics solutions from our underlying platform? If this sounds interesting to you, then I'd encourage you to have a look at my case study to understand how I have utilized DBT to try and address these questions. I want to thank Zoltan C. Toth and Miklos Petridisz whose course on Udemy inspired me to experiment with DBT and run this interesting case study. #learninganddevelopment #learning #dbt #dataengineering #dataanalytics #bi #mi #bireporting #mireporting #womenindata #womenintechnology #continuouslearning #casestudy #data #tech

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Week3 content: Distributed Processing fundamentals What is Mapreduce?   Mapreduce is a framework which is used for processing large amounts of data. It breaks down the bigdata tasks into smaller chunks and process them in parallel. Map phase: map phase works on individual machine and this gives us parallelism. More the mappers we have more the parallelism Reduce phase: aggregation on one machine. try doing more work at mapper end and try doing minimum work at reducer end. Partitions: it is used to distribute the data in the key,value pair forms Map, Combiner, Shuffle, Sort and Reducer What is Spark? Spark is a general purpose inmemory compute engine. Spark can replace or compared with mapreduce not with Hadoop.  Spark keeps the data in memory and uses that memory when required. Spark needs storage and resource manager. Spark has 2 layers -- Higher Level APIs and Spark Core APIs Apache Spark Vs Databricks: Databricks is a tool that is built on top of Spark Spark Execution Plan: Logical plan Physical plan DAG A special thanks to Sumit Mittal Sir for exceptional teaching, and gratitude to the TrendyTech team for their unwavering support and assistance.

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Microsoft Fabric revolutionizes analytics with a unified, AI-enhanced, cloud-integrated data platform. Here are 5 reasons why - 𝟭) 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺: Microsoft Fabric is introduced as a unified data analytics platform, integrating various aspects like data engineering, data science, and data governance into a single, cohesive experience. It provides a one-stop solution for data management and analysis within the Microsoft ecosystem. 𝟮) 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗻𝗱 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: The platform utilizes a unique architecture called "One Lake," where data is stored in an open Delta Parquet format. This approach standardizes data storage across different tools and services within Microsoft Fabric, allowing seamless data accessibility and querying across cloud platforms like Azure, AWS, and Google Cloud. 𝟯) 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: Microsoft Fabric offers a unified experience by combining all necessary tools for data professionals into one interface. This includes data pipeline orchestration, machine learning model training, and key metric definitions. Additionally, it supports collaborative and ad hoc data analysis within Microsoft 365 for business users. 𝟰) 𝗕𝘂𝗶𝗹𝘁-𝗶𝗻 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗜 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Security, compliance, and governance are integrated features, ensuring data protection across the platform. Microsoft Fabric also incorporates AI elements like Copilot to enhance productivity, aiding in tasks such as writing SQL queries and automating report generation. 𝟱) 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗨𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Features like "shortcuts" enable users to access and query data without needing to migrate it between different cloud services. This functionality, combined with the virtual file and table capabilities, aims to streamline operations and make data handling more efficient and user-friendly across various devices and platforms. While major organisations are currently evaluating the advantages Microsoft Fabric can offer to their organisational data strategies, one thing is for sure - It's bound to pose a threat to current major services and tools like tableau, AWS, etc by promising a 1 single platform that can bring all such capabilities together under 1 roof. #microsoftfabric #onelake #cloudservices #artificialintelligence #storage #dataanalytics #dataengineering #businessintelligence #maintenance #governance

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🚀 If you are preparing for a Databricks interview or just looking to strengthen your knowledge? Here are some key interview questions and answers that can help you stand out your next interview. Let’s dig in! 🚀 1. What is Databricks ? ⚡️ Answer: Databricks is a unified data analytics platform built on top of Apache Spark. It provides a collaborative environment for data engineers, data scientists, and business analysts to work together on data processing, machine learning, and analytics. 2. What is Delta Lake? ⚡️ Answer: Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to data lakes. It ensures data reliability by providing features like schema enforcement, time travel, and upserts. 3. How does Databricks handle real-time data processing? ⚡️ Answer: Databricks handles real-time data processing using Structured Streaming, a scalable and fault-tolerant stream processing engine built on Apache Spark 🌟. 4. What is MLflow and how is it integrated with Databricks? ⚡️ Answer: MLflow is an open-source platform for managing the complete machine learning lifecycle, including experiment tracking, model packaging, and deployment. 5. How does Databricks optimize performance for big data workloads? ⚡️Answer: Databricks optimizes performance for big data workloads through several features: 1️⃣ Auto-scaling Clusters: Automatically scales clusters up and down based on workload demands, optimizing resource usage and cost. 2️⃣ Caching: Uses in-memory caching to speed up data processing. 3️⃣ Data Skipping: Skips irrelevant data blocks during query execution, reducing I/O operations. 4️⃣ Adaptive Query Execution: Dynamically adjusts query plans based on runtime statistics to improve performance. 🚀 #Databricks #BigData #DataEngineering #DataScience #MachineLearning #ApacheSpark #DeltaLake #MLflow #TechInnovation #DataDriven #CloudComputing

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Today's Learning ✌️ While going through one PBIX file. I saw that data was more than 5 Million Rows in Fact Table where Import Mode was used as Connectivity Mode. Snowflake Schema was used. But PBIX file was only of 25 MB. How Come? 🤔🤔🤔 Reason: ⬆️ 1) Data was Fetched of only last 6 months not all ✅ 2) Unwanted Columns where removed from Table ✅ 3) Grouping was done ✅ It looks very simple but Very Important observation real time work perspective 👍 Hope this helps 🙏 #powerbi #powerbideveloper

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As a continuing trend, major data platform vendors have been keep adding features around data quality and observability in their core platform. While the "in-house" requirements for data quality are obvious and doesn't need any explaining, these capabilities are even more crucial for the data providers as their data contracts with their customers/consumers will have agreed levels of quality, freshness etc. Any degradation here would lead to direct monetary losses and business credibility. Hence it is important to have these data quality capabilities built into the systems and pipelines, executed and monitored at scheduled intervals, issues are triaged and resolved on time. Few months back, Snowflake has introduced data quality capabilities in the form of data metric functions (DMF). These functions comes in two flavors.   1. The pre-built system DMFs can be used as-is and are available through SNOWFLAKE.CORE packages. These perform various data hygiene checks and freshness checks. 2. The custom ones can be built based on business rules and specific requirements. These functions can be scheduled, executed with results captured in built in event tables, which can be further utilized for triaging and remediation. The 1-slider below summarizes the capabilities and features as a quick reference. What are your experiences in taking it to Production? #Snowflake #DataQuality #Observability #DataMetricFunctions #WrittenByHuman

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I've prepared 30 PySpark Interview questions which were asked in many companies like #tigeranalytics,#kpmgindia, #globant #accenture, etc. 1. Merge two Dataframes using PySpark | Top 10 PySpark Scenario-Based Interview Question| 2. Explode columns using PySpark | Top 10 PySpark Scenario-Based Interview Questions | 3. Solve using Regex using PySpark | Top 10 PySpark Scenario Based Interview Question| 4. Skip line while loading data into dataFrame| Top 10 PySpark Scenario-Based Interview Question| 5. Count rows in each column where NULLs present| Top 10 PySpark Scenario Based Interview Question| 6. How to handle multi delimiters| Top 10 PySpark Scenario-Based Interview Questions | 7. Solve using REGEXP_REPLACE | Top 10 PySpark Scenario-Based Interview Questions | 8. Check the Count of Null values in each column 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐥𝐢𝐧𝐤 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭: https://lnkd.in/dDvufQAj Here is the full paid course - New questions https://lnkd.in/dtWNG7Ku #pyspark #sparksql #geekcoders

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Hello Connections, Tiger Analytics hiring #Azure #DEVOPS #Architect Please share max & Mention their name in the comments. Experience level: 9+ years Apply here —>https://lnkd.in/gWjaW5b7 Technical Skills Cloud : Azure Containers/Orchestration : Docker, Kubernetes, AKS, EKS Infra as Code : Azure Biceps CI/CD Tool : Azure DevOps Repository Management : Artifactory, Nexus, JFrog Code Analysis : SonarQube Security Tools : Fortify/Blackduck/Checkmarx Monitoring / Security : Grafana, Prometheus Database : MySQL, MongoDB, DynamoDB Scripting/Languages : Shell, Python, Groovy, YAML Skills Summary 1. Drive DevOps adoption by implementing CI/CD tool sets and best practices for automation. 2. Managing cloud - Azure 3. Hands on experience with containerization and orchestration platforms using Docker, and Kubernetes on Cloud (AKS, EKS) 4. Automating tools/infra provisioning using Ansible, Terraform 5. Automate provisioning and repetitive tasks using Terraform and Python. 6. Provide mentorship and guidance to team members. 7. Experience in developing proposals and responding to Requests for Proposals (RFPs). Identify emerging technologies and develop relevant assets for the organization. #Jobs #TigerAnalytics #Devops #AzureCloudplatform #AzureDevops #Referal #Job #AzureJob #CICD

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Power BI in Hindi- DAX Function: RANK - How It Differs from RANKX| How to break ties: https://lnkd.in/gkce99ti #powerbiinterview #PowerBI #dax #powerquery #AmitChandakDA #businessintelligence #PL300 #powerplatform #bi #powerbidesktop #datatodiscovery #powerbitutorial

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You might have come across the words like Datawarehouse and Data Lake multiple times if you are working in Data and Analytics domain and it could be bit confusing at times to differentiate between these two.  So, here i am highlighting the key differences between these two to understand them better DataWareHouse(DWH) and Datalake(DL) 𝟭.𝗦𝗰𝗵𝗲𝗺𝗮:   > Data warehouse follows the schema on write (Predefined schema before writing the data) approach whereas    > Datalake maintains the schema on read  𝟮.𝗧𝘆𝗽𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮:   > DWH is primarily built with structured data, on the other hand   > DL comprises of both structured and unstructured data 𝟯.𝗔𝗴𝗶𝗹𝗶𝘁𝘆:   > DWH is less agile cause of its Schema on read nature and   > DL is more agile as it accepts the raw data without a predefined structure 𝟰.𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴:   > DWH follows ETL method, means Extract Transform and Load (gives no option to apply the transformations after the data load) but   > DL gives the flexibility with ELT approach which means you can bring the data to datalake first in its raw form and think of transformations to be applied in later stages. 𝟱.𝗖𝗼𝘀𝘁:   > DWH are typically more expensive because of optimization for complex queries   > DL provides cost effective storage solutions, but cost can rise with huge data processing. #bigdata #dataengineering #interview #DataWarehouse #DataLake #DataAnalytics #DataManagement

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#azure #bigdata #dataengineer #dataanalyst Hi Connections, Today, we will see how to apply SCD Type 2 using PySpark. customer_dim_data = [ (1,'ram','arwal','india','N','2022-09-15','2022-09-25'), (2,'shyam','patna','india','Y','2023-08-12',None), (3,'nikita','delhi','india','Y','2023-09-10',None), (4,'rakesh','jaipur','india','Y','2023-06-10',None), (5,'ayush','NY','USA','Y','2023-06-10',None), (1,'ram','gurgaon','india','Y','2022-09-25',None), ] customer_schema= ['id','name','city','country','active','effective_start_date','effective_end_date'] customer_dim_df = spark.createDataFrame(data= customer_dim_data,schema=customer_schema) sales_data = [ (1,1,'ram','2023-01-16','gurgaon','india',380), (77,1,'ram','2023-03-11','bangalore','india',300), (12,3,'nikita','2023-09-20','delhi','india',127), (54,4,'rakesh','2023-08-10','jaipur','india',321), (65,5,'ayush','2023-09-07','moscow','russia',765), (89,6,'rajat','2023-08-10','jaipur','india',321) ] sales_schema = ['sales_id', 'customer_id','customer_name', 'sales_date', 'food_delivery_address','food_delivery_country', 'food_cost'] sales_df = spark.createDataFrame(data=sales_data,schema=sales_schema)

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🎉 HDFS vs. Data Lakes: A Fun Dive into Big Data Storage! 🚀 Hey fellow Data Enthusiasts! 🌟 It's often said that cloud storage is more cost-effective than HDFS. But is that really true? Let’s dive in and find out🏄‍♂️ 🌈 HDFS (Hadoop Distributed File System): In HDFS, data is stored across multiple servers, or nodes, which provide both storage and computing power. To increase storage capacity in HDFS cluster, you add more nodes. This means that if you need more storage, you also have to add more computing power, even if it's not necessary. Imagine you're at an all-you-can-eat buffet 🍽️, but you have to pay for a drink every time you get a plate. That's HDFS for you! 🧠 Storage and computing power are tightly coupled, so if you want more storage, you have to add more compute power too! 💻📦 It's efficient for big data processing but sometimes feels like buying a new car 🚗 just because you need a new tire! 😂 💡 Data Lake Cloud Storage: Cloud-based data lakes offer more flexibility by separating storage from computing resources, with on-demand scalability for compute resources. Picture a magical pantry 🧚‍♀️ where you can pick as many cookies 🍪 (data) as you want without having to buy extra milk (compute power) unless you actually need it. That's your data lake! 🌊 With cloud-based data lakes, storage and computing are decoupled. Need more storage? Just add space! Need more computing power? Scale up only the compute! It's like paying for just the cookies you eat! 🎯 💸 Cost-Effectiveness: HDFS is like buying in bulk at a wholesale club—great if you consume a lot, but sometimes you end up with more than you need. Data lakes, especially in the cloud, let you pay-as-you-go, keeping costs low and efficient. It's like choosing between a big box store and a bespoke bakery! 🍰 🔄 Elasticity: In today's fast-paced world, flexibility is key! 🕺💃 Data lakes are like yoga masters—super flexible and able to stretch to meet your needs without breaking a sweat. 🧘‍♂️🧘‍♀️ Need to crunch data at scale? Up goes the compute! Just storing? Keep it simple and cost-effective. It's all about adapting to your needs, making every day a good data day! 📊🌞 So, next time you're considering your data storage strategy, remember: HDFS is powerful but comes with a package deal, while data lakes offer flexible options. Choose wisely and make your data day even better! 💼💥 What’s your take on this data showdown? Share your thoughts! #DataStorage #HDFS #DataLake #BigData #CloudComputing #TechTalk #DataDay

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Enter the ultimate face-off: Apache Spark vs. Apache Hadoop. These two powerful tools serve distinct purposes.                Know more ......          👇👇👇👇👇👇👇👇👇👇👇👇👇 https://lnkd.in/gYAFCM7b #Apachespark #ApacheHadoop #DataProcessing #RealTimeAnalytics #BatchProcessing #BigDataTools #DataAnalytics #CloudComputing #DataStorageSolutions #MachineLearning #DataArchitecture

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An under appreciated reason why data processing companies like Confluent, Databricks, Splunk etc get really big is how data scales. Data usage in a company scales O(EQS), if we use Big-O notation, E=number of employees using the data, Q=number of queries being run , S=size of data queried. The reason customer ACVs can get absolutely massive is that if you’ve built an incredible product you will be able to process more data if the customer does even one of the 3: - store more data - add more employees who want to work with the data - have more computations being done on the data Most of the time, companies that are growing have all 3 increasing simultaneously. Ofcourse, because data tools are sticky this growth also persists and even when employee growth slows down, often more data and uses of the data have emerged If you’re building any tools, or even side projects that involve data processing, I’d love to learn more and talk to you!

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Document analysis using LLMs can be complex and may require a combination of strategies to achieve accurate results. The output can vary a lot based on the quality of data, contextual understanding, content processing/chunking, and model selection. I still feel Human feedback loop at different stages is an extremely important factor in achieving optimal results. #DocsIQ #llmapps #aws #amazonwebservices #amazonbedrock #serverless

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🚀 Data Engineering in Action: Oracle CDC to Snowflake with Confluent Kafka & Dynamic Tables In my latest project, I had the opportunity to work on a seamless data pipeline by integrating a Change Data Capture (CDC) Oracle database with Confluent Kafka to Snowflake, where we built dynamic tables to enhance our data warehouse architecture. 🔗 Key Highlights: Utilized CDC on Oracle to capture real-time changes and stream them into Snowflake. Leveraged Confluent Kafka as the data streaming platform for efficient, scalable, and real-time data ingestion. Built dynamic tables using SQL on Snowflake to create a flexible and scalable data warehouse architecture that could adapt to changing business needs. Ensured low-latency, high-throughput data movement from Oracle to Snowflake, enhancing overall data availability and analytical capabilities. 💡 This experience reinforced the power of real-time data streaming and the flexibility that Snowflake offers for building robust data warehouses. It was an exciting challenge, and I’m looking forward to more projects that push the boundaries of data engineering and architecture. #DataEngineering #Kafka #ConfluentKafka #Snowflake #Oracle #CDC #DataWarehouse #RealTimeData #SQL #BigData

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