Datashift’s Post

Through completing this lab, I've learned to Use Delta Lake with Apache Spark in Azure Synapse Analytics. This enabled me to perform data operations with relational semantics on top of a data lake. Exploring Delta Lake's functionality, I discovered its capability to process both batch and streaming data, paving the way for the creation of a Lakehouse architecture with Spark. https://lnkd.in/eGJ9xpNM #Azure #DataEngineering #DeltaLake

Today I woke up and I realized that articles that I wrote in 2022 were endorsed by Abhisek Sahu, this motivates people to continue sharing knowledge. I never expected my articles to be shared on LinkedIn. I would like to share some of my content: 📗 𝐀𝐫𝐭𝐢𝐜𝐥𝐞𝐬: 1. #Building an end-to-end data pipeline using Azure Databricks [ENG] 🔗 https://lnkd.in/gjy2MMq3 2. Deploy Azure #Databricks using #Terraform [ENG] 🔗 https://lnkd.in/efQtW8hE 3. Spark Conceptos Claves [SPA] 🔗 https://lnkd.in/efSranS9 4. Arquitectura de Apache #Spark [SPA] 🔗 https://lnkd.in/e3ibi_6E ▶ Videos: 1. Building end-to-end #data #pipeline using #opensource technologies. [ENG] 🔗 https://lnkd.in/eKHvu-KN [SPA] 🔗 https://lnkd.in/emS9V3u5 2. #Realtime project using #GCP #technologies [SPA] 🔗 https://lnkd.in/emSiqR3u 3. Scrapping over instagram [SPA] 🔗 https://lnkd.in/eup7xgxA 🤝 Follow me👨💻 and Abhisek Sahu for a regular curated feed of data engineering insights and valuable content!

Benchmark to make a fair comparison between BigQuery, Spark (on Dataproc Serverless) and Dataflow. Hopefully this content will help you choose the tool that best fits your use case and your team, so that you can take the most of Google Cloud’s Big Data processing capabilities. Google #dataflow #spark #bigquery #bigdata #dataprocessing

🚀 Public Preview of Native #Vector Support in #Azure #SQL Database! 🚀 With the rise of #AI and machine learning, handling vector #data is crucial for applications like #semantic search, recommendation systems, and #GenAI chats based on #LLM with #RAG. Azure SQL now supports a dedicated Vector data type, simplifying the creation, storage, and querying of vector #embeddings directly within a #relational #database. You can read the 📢 announcement at: https://lnkd.in/ga4kzw7t This development eliminates the need for separate vector databases and related integrations, enhancing #security, streamlining data #architecture, improving #performance, and reducing overall complexity. 🎓 Learn all about #Native Vector #Support in Azure SQL by checking the official documentation here: https://lnkd.in/gwHAF_mC 🔍 Explore #GitHub repositories with: 👉 Code samples illustrating key technical concepts and demonstrating how to store and query #embeddings in Azure SQL data at: https://lnkd.in/gnPdiG_2 👉 End-to-end #samples showcasing workflows that integrate Azure SQL data with popular AI #application components like #Promptflow, #LangChain, #Chainlit, #Semantic #Kernel, and #Redis, both inside and outside of Azure at: https://lnkd.in/gv_gzPQK #Microsoft #MicrosoftAzure #AzureSQL

The only place in the world where Laziness is appreciated, Apache Spark 😎 But how it helps us? . . . . . ♦ Suppose you have a very big file in data lake (HDFS / Azure ADLS Gen2 / AWS S3) let's say 10 TB & you want to print only first 10 lines.   ♦ If Spark is not lazy, then it will first load the entire file to cluster in the form of an RDD or Data frame and then prints first 10 lines. Although we have achieved what we wanted but it's not the optimized approach. Just to print 10 lines you loaded billions of records into memory, which simply does not make any sense.   ♦ On the other hand, when spark is lazy, then whatever transformations you apply, those will become part of DAG. (Directed Acyclic Graph, in simple words DAG is just an execution plan and execution will trigger when you fire an action). Now spark will analyze the DAG and figure out what you want and depending upon that, it will shuffle the transformations in DAG. So, if we consider the same example, then spark will only load those 10 lines into memory which you wanted to print, not all the records. Which makes a lot of sense and its optimized too. So, it’s a win-win situation for us.   Stay tune for more such content. Credits: Sumit Mittal & TrendyTech #bigdata #spark #apachespark #distributedprocessing #trendytech #hiring #HR

In this new post of our ongoing series, we’ll explore setting up Azure Cosmos DB for NoSQL, leveraging the Vector Search capabilities of AI Search Services through Microsoft Fabric’s Lakehouse features. Additionally, we’ll explore the integration of Cosmos DB Mirror, highlighting the seamless integration with Microsoft Fabric. It’s important to note that this approach harnesses … Continue reading “Fabric Change the Game: Embracing Azure Cosmos DB for NO SQL” [https://lnkd.in/gA89X2hh] #MicrosoftFabric #MSFTAdvocate

Are you looking to work with Delta Lake but want to avoid the cost and complexity of Databricks? The Rust-based "delta-rs" crate provides a compelling alternative, allowing you to create and append to Delta Lake tables without the need for a Spark cluster. Some key benefits of the delta-rs approach: **Cost Savings**: Run Delta Lake operations in cheaper, more scalable environments like AWS Lambda. **Operational Simplicity**: Abstracts away the complexity of Spark cluster management. -**Flexibility**: Use delta-rs in diverse contexts, like Kafka message ingestion. Learn more about how you can implement Delta Lake without Databricks in our latest technical blog post: https://lnkd.in/gncb_Dkj #DataEngineering #DataLakes #DeltaLake #Rust

What is azure databricks and It's main components? Azure Databricks is a powerful, cloud-based platform that combines the capabilities of Apache Spark with  Microsoft Azure to help data engineers and scientists process big data and  build machine learning models collaboratively. Azure Databricks architecture consists of several key components that work together to provide a unified analytics platform. Here’s a simple breakdown: Workspace: The main place where you can work on projects. It has  notebooks (for code and notes), libraries, jobs, and dashboards. Clusters: Groups of virtual machines that run Apache Spark tasks. They can  scale automatically. Jobs: Scheduled tasks that run code or scripts. They automate data  processing. Notebooks: Interactive documents for writing code, visualizing data, and  taking notes. Support multiple languages (Python, SQL, Scala, R). Delta Lake: A storage layer that ensures data reliability with features like  ACID transactions and schema enforcement. Data Sources: Can connect to various data sources (e.g., Azure Blob Storage, SQL databases). Security and Compliance: Uses Azure Active Directory for user authentication and role-based access control. Data is encrypted. Integration with Azure Services: Works well with other Azure services like  Azure Machine Learning and Power BI. #azuredatabricks #azure #microsoft #dataengineering #cloudcomputing

Mastering Data Ingestion with PySpark: Handling Multi-Source Pipelines with Precision Dealing with CSV, JSON, and SQL in a single pipeline can feel like trying to herd cats—each format behaves differently. For my latest project, I used PySpark’s connectors to pull from Azure Blob Storage, Event Hub, and SQL Server, efficiently processing large datasets. PySpark’s ability to scale and optimize these reads, especially with partitioning, is a game changer for performance. And let’s be honest—if it weren’t for PySpark, I’d probably still be manually wrangling those files. 😅 #Azure #DataEngineering #PySpark #DataIngestion #BigData

📣 I'm thrilled to share some key insights and skills I've gained from my learning journey with #AzureDatabricks and #ApacheSpark! Key Skills and Insights ❇ Understanding Spark Architecture: Learned the core components and how Spark operates within a cluster. ❇ Setting Up Spark Clusters: Gained hands-on experience in creating and configuring Spark clusters on Azure Databricks. ❇ Exploring Use Cases for Spark: Discovered real-world applications in data analytics, machine learning, and stream processing. ❇ Data Processing with the Dataframe API: Mastered the Dataframe API to process and analyze data from various sources. ❇ Visualizing Data with Spark: Learned to create visualizations to turn raw data into meaningful insights. This journey has been incredibly rewarding, enhancing my problem-solving skills and boosting my confidence in handling large-scale data tasks. #MicrosofAzure #DataScience #BigData #MachineLearning #DataAnalytics #DataEngineering #CloudComputing #TechLearning #LearningJourney #CareerGrowth #Python #R #SQL #SparkSQL #ApacheSpark #AzureDataFactory #AzureSynapseAnalytics #DataLake #AI #Analytics #ETL #BusinessIntelligence #DataVisualization #RealTimeData #DataProcessing