SightSpectrum’s Post

🚀Wondering how to drive better ROI from your #CXStrategy? Think about leveraging your existing #CustomerData for better comprehension of your customers' needs. From data engineering to #BigDataAnalysis, we are helping businesses make better sense of their #Data. Explore Altudo’s #DataAnalysis expertise: https://lnkd.in/g8vPqhJY #BigData #DataManagement #CustomerInsights #CustomerJourney #AltudoServices

Hey everyone! Let's dive into the exciting world of data transformation! I will be sharing the basics of 𝙀𝙏𝙇 (𝙀𝙭𝙩𝙧𝙖𝙘𝙩, 𝙏𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢, 𝙇𝙤𝙖𝙙) and 𝙀𝙇𝙏 (𝙀𝙭𝙩𝙧𝙖𝙘𝙩, 𝙇𝙤𝙖𝙙, 𝙏𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢) – the two ways we make sense of data and how we turn raw data into useful information. ☑️ Both 𝙀𝙇𝙏 and 𝙀𝙏𝙇 have their strengths and are 𝙘𝙝𝙤𝙨𝙚𝙣 𝙗𝙖𝙨𝙚𝙙 𝙤𝙣 𝙨𝙥𝙚𝙘𝙞𝙛𝙞𝙘 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨 and 𝙩𝙚𝙘𝙝𝙣𝙞𝙘𝙖𝙡 𝙧𝙚𝙦𝙪𝙞𝙧𝙚𝙢𝙚𝙣𝙩𝙨. ✔️ 𝙀𝙇𝙏 offers 𝙛𝙡𝙚𝙭𝙞𝙗𝙞𝙡𝙞𝙩𝙮 and is suited for 𝙡𝙖𝙧𝙜𝙚-𝙨𝙘𝙖𝙡𝙚 𝙙𝙖𝙩𝙖 environments. ✔️ 𝙀𝙏𝙇 provides a 𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝 for consistent and predefined data transformation tasks. #DataManagement #BigData #ELT #ETL #DataWarehouse #DataTransformation #BusinessIntelligence #DataAnalytics #TechTrends #DataProcessing #BigData #DataStrategy 

Data Pipelines: The Unsung Heroes of Data Analytics Data scientists and analysts often get the spotlight when it comes to data insights. But let's not forget the silent heroes behind the scenes - data pipelines! These pipelines are the critical infrastructure that continuously move data from its origin to the point of analysis. A well-designed data pipeline ensures clean, reliable data is readily available for analysis, leading to faster and more accurate insights. What are your experiences with data pipelines? Share your thoughts and challenges in the comments! Follow Sujit Prasad for more content. #dataengineering #datapipelines #dataanalytics #datainsights #dataenthusiast

🔍 Your Expertise Needed: Data Engineering / Analytics Glossary Review! Learning important terms can be challenging with so many different sources! That's why we've curated a glossary of essential data engineering and analytics terms for easy reference. But we need your insights! 👥 Calling all data professionals: (That’s you! 😀) We want your feedback to refine our glossary. Missing any key terms? Thoughts on our definitions? ✏️ Share your expertise in the comments. Let's collaborate to create the most reliable resource for our community. 👇🏻 Check out the list and join the discussion below! #dataanalytics #dataglossary #learndata #dataengineering #analyticsengineering #peerreview #communityengagement #anelyticsengineering https://lnkd.in/e8rZSYsF

Unlocking data gold: the journey from raw data to informed decisions. In the quest to unlock the treasures hidden within raw data, data engineering serves as the architect of our journey. Our data engineering team is equipped with the expertise and innovation to tackle it all! Interested? Contact us by email [email protected] or take a look at our website https://lnkd.in/evAcA3Tr #DataEngineering #DataScience #Analytics #MachineLearning #DecisionMaking #DataDriven #Innovation

🔍 Understanding Data Storage: Schema on Read vs. Schema on Write 📝 In the world of data management, two key approaches shape how we handle data: Schema on Read and Schema on Write. Let's break down what they mean: 1. Schema on Write: ✅ In this traditional approach, data is structured and organized at the time of ingestion into the database. ✅ It enforces a strict structure, ensuring data quality and consistency from the start. ✅ Great for scenarios where the data schema is well-defined and unchanging. 2. Schema on Read: ✅ Here, data is ingested without a predefined structure and is only organized when it's read or analyzed. ✅ It offers flexibility and agility, as data can be repurposed for various analyses without upfront constraints. ✅ Ideal for big data, unstructured data, and situations where schema evolves over time. Both approaches have their strengths and use cases. Your choice depends on the nature of your data and your analytics needs. Which one do you use, and in what scenarios? Share your thoughts! 💡 #DataManagement #BigData #Analytics #SchemaOnRead #SchemaOnWrite #DataWarehousing