Sateesh Pabbathi’s Post

2. What is a data pipeline, and why is it important in data engineering? Answer: A data pipeline is a series of automated processes that ingest, process, and store data from multiple sources to a destination like a data warehouse or data lake. It is crucial in data engineering because: It ensures the continuous flow of data across systems. Automates data transformations and ensures data integrity. Allows for real-time or batch processing depending on business needs. Facilitates scalability, making it easier to handle large volumes of data with minimal manual intervention. #BigData #DataLakes #DataWarehouse #DataEngineering #DataStorage #DataStrategy #Analytics #TechTalk

Data Engineering: Incremental Data Loading Strategies: Outlining strategies and solution architectures to incrementally load data from various data sources. Continue reading on Towards Data Science » #MachineLearning #ArtificialIntelligence #DataScience

Recently, I have been focusing a lot on data governance and architecture. Data quality is one of the key point of discussion, so I wanted to share my thoughts of how to implement data quality in a lakehouse platform using Databricks #dataengineering #dataquality #datagovernance

Advanced Data engineering with Data bricks #databricks #azuredataengineering #data #integrations

Just completed a comprehensive data engineering course covering data gathering, #integration, #cleaning, preparation, #visualization, and #analysis—ready to tackle real-world data challenges!

🔔 Critical aspects of any Data Initiative- Data Mapping and Data Models The video is about- ➡ Source to Target Mapping ➡ Landing, Stage and Target Layers ➡ Enterprise Data Warehouse Model ➡ Data Mart and Types of Dimensions ➡ Data Lake ➡ Statistical Models for Data Science Hope you found this information to use. Please contact me with questions and project collaborations. Thanks #dataanalytics #enterprisedata #datamapping #datamodeling #etl #datalake #datascience #aiml #datainitiatives #architecture #datamart #businessintelligence #datareporting #edw #partymodel #starschema

Improve #AzureCosmosDB performance with effective data modeling. Understand embedding vs. referencing data in this quick video: https://lnkd.in/e-iX8khK ⚙️🔍 #CloudDatabase

💭 Imagine you’re running a business, and data is flowing in from all directions like customer interactions, sales reports, social media feedback, and much more. You are aware that this data holds the key to better decisions, but managing it effectively? That's the real challenge. 💡 Whether it's structured, unstructured, or somewhere in between(semi-structured), choosing the right data storage architecture is crucial. The wrong choice could mean missed insights, slow performance, rising costs and loosing potential clients. But with so many options in the market like Data Warehouses, Data Lakes, and now the Data Lakehouse. How do you know which one is right for your business? 🎥 In my latest video, I break down these architectures and help you discover which one fits your unique data needs. Whether you're just starting out or looking to scale, this guide will help you make an informed decision. 👉 Check it out here: https://lnkd.in/d5vwCywW Stay tuned for more updates, and feel free to connect if you'd like to dive deeper into my process or discuss insights. I’d love to hear your thoughts and any suggestions on how we can continue unlocking even more value from our data. #DataArchitecture #AI #AIInsights #DataManagement #BusinessIntelligence #DataInsights #TechStrategy #DataWarehouse #DataLake #DataLakehouse #MachineLearning #GenerativeAI #DeepLearning #NaturalLanguageProcessing #BigData #BIReport #BIDashboard #Insights

Looking to derive better value from your data? 📈 We have an expanding data engineering and data analytics capability and are now a Databricks consulting and implementation partner. Get in touch if you want to find out more about how we can help unify data management across your teams and systems. #DataEngineering #DataAnalytics #Databricks #Technology

#Technology #DataAnalytics #DataDriven Building Scalable Data Platforms: Data Mesh trends in data platform design Continue reading on Towards Data Science » #MachineLearning #ArtificialIntelligence #DataScience