Sachin Tripathi’s Post

So many #enterprises are undergoing a #digitaltransformation right now with many of them moving from on-prem to cloud and some vice versa. One of the key obstacles in this exercise is the need to consolidate data stores and migrate them! Modern datastores are designed for particular usage patterns and #data models, so when the usage patterns change so does the datastore technology. Database migrations are often essential due to factors such as security vulnerabilities, deprecated APIs, outdated software, controlling costs or the need to enhance performance/functionality. These migrations are time taking exercises and often rely heavily on Backend Engineers & Data Engineers working in tandem with stakeholders as various business functions like Business Intelligence / #analytics, Product APIs and #ML services could be impacted by downtime.

"Excited to share a recent project where we designed a global stock aggregation batch processing pipeline! 🌎📈 Our on-premises database had location-specific tables (London, Mumbai, Bangalore, Hyderabad), each containing valuable item stock data. The challenge was to aggregate this data daily. Here's how we did it: Extracted data from on-prem databases. Transformed data to a common format. Aggregated data daily by item and date. Stored it securely in Azure Data Lake Storage Gen2. Scheduled the pipeline to run automatically. Ensured error handling and data quality. 🚀 #DataEngineering #Azure #BigData #Aggregation"

💡𝐀𝐦𝐚𝐳𝐨𝐧 𝐃𝐲𝐧𝐚𝐦𝐨𝐃𝐁 ⭐DynamoDB is a managed #nosql with seamless scalability and predictable performance that shields #users from the complexities of manual setup. ⭐DynamoDB is primarily a key-value #store in the sense that its #data model consists of key-value pairs in a schemeless, very large, non-relational table of rows (#records). ⭐DynamoDB  focuses on innovation and optimizing #costs with a fully managed serverless #database that automatically scales up and down to fit your needs. ⭐DynamoDB offers some of the best features such as built-in #security, backup and restores, and in-memory caching. ❎𝐌𝐚𝐣𝐨𝐫 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 ▪️Table - The collection of #items is known as a #table. ▪️Items - An item is a group of #attributes that is uniquely identifiable among all of the other items. ▪️Attributes - An attribute is a fundamental data #element, something that does not need to be broken down any further. ❎𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 ▪️Performance and Scalability - provides #virtually unlimited storage, and users can utilize it as required. ▪️Enterprise Ready - supports ACID transactions to enable #build business-critical #applications at scale.  ▪️Seamless Data Replication - allows easy #recovery from any disaster and avoids #service disruptions. ▪️Serverless - does not require users to maintain any underlying #infrastructure. ❎𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 ▪️Developing software applications ▪️Banking & finance ▪️Media & entertainment ▪️Gaming ▪️Seamless retail experiences 💙#cloudclicksbyathira

#AzureDaily 📢Exciting news! Azure SQL Database now supports invoking external REST endpoints for seamless data querying - an essential step in modern data integration workflows 🚀. New JSON integration features and security enhancements included. ✅ #AzureSQL #MicrosoftAzure #DataIntegration

#opentowork #immediatejoiner #AzureDataEngineer #LookingforChange My domains as an Azure Data Engineer Azure Data Factory -Builds Data Ingestion Pipelines, Linked Services, Integrated Runtime (Mgv Net), Self-Hosted IR, Metadata driven and event Driven Ingestions, ETL and ELT loads ability to deal with different source and target formats, Incremental Delta load using watermarks and Upsert logic. Azure Synapse - Massive Parallel Processing ,Big Data Analytics, Data Warehousing, Distribution techniques (Roud-Robin, Replicated, Hash Based),Clustered and Non-clustered Indexing,SQL Based Architecure (Contol and Compute nodes).Query Optimisation.External tables referencing various data source formats(csv,parquet,.txt).Dedicated SQL pool , serverless SQL pool,Rest-Apis,Key Vaults,Managed Access Identity.

#Data_Engineering_insights:Day-20 ����key terminologies related to Azure Data Factory 🔸Pipeline: A sequence of data-driven activities or tasks that are organized and executed to move and transform data from source to destination. 🔸Activity: A processing step within a pipeline, such as data movement, data transformation, or data analysis. 🔸Data Flow: A graphical representation of a data transformation process that defines the data flow from source to destination. 🔸Linked Service: A configuration that allows Data Factory to connect to external data sources or destinations. 🔸Dataset: A named view of the data that simply points or references the data you want to use in your activities. 🔸Trigger: An event that initiates the execution of a pipeline or a specific activity within a pipeline. 🔸Integration Runtime: A compute infrastructure used by Azure Data Factory to provide data movement and data transformation capabilities across different network environments. 🔸Linked Integration Runtime: A specific type of Integration Runtime used for securely connecting to external services. 🔸Data Flow Debug Run: A mode that allows you to test and debug your data flow logic before publishing it. 🔸Data Flow Debug Window: A visual interface for monitoring and debugging data flow activities. These terms are essential for understanding and working with Azure Data Factory. Follow Vidhyasagar SB for more valuable insights ✌️ #dataengineering #azuredataengineer #azuredatafactory #openforopportunities #openforjobs

Database design is an essential aspect of data management, and it plays a critical role in the overall performance, security, and scalability of the system. A well-designed database can improve:  - Data integrity  - Reduce data redundancy  - Increase query performance MongoDB #database #design #robust #development #technology #mongodb #data #trending #future #nxtr #nextrevol

Hello #DataFam yesterday I had an interview for Azure Data Engineer role, in which the interviewer asked me a scenario based question which I answered but he was not convinced with the answer, so I thought to share that scenario here, Scenario - lets suppose you have a data source(oracle db), then while ingestion following scenarios can happen 1] sometimes data may come in partitions - lets say 10 partitions 2] sometimes data may come in batch format 3] sometimes data may come in incremental loads So, you have to handle all these scenarios using 1 single pipeline only, then what will be your approach I had prepared such scenarios using google and chatgpt but those are very generic and the real world scenarios are much more complex, so feel free to share your approach here, I will also get to learn from your perspectives and if you know any resource where I can prepare such scenarios do let me know. Thanks. #dataengineering #data #datascience #dataanalysis #azure #azuredataengineer

Integration Runtimes(Azure Data Factory): ⚡ ⏩FAQ in Azure Data engineer Interviews.���️ ✳️An Integration Runtime (IR) is the compute infrastructure used by Azure Data Factory to provide data integration capabilities such as Data Flows and Data Movement. ✳️ It has access to resources in either public networks, or hybrid scenarios (public and private networks). ================================ ⭐There are 3 types to choose from: ⏩Azure Integration Runtime: ▪️It is managed by Microsoft. ▪️All the patching, scaling and maintenance of the underlying infrastructure is taken care of. ▪️The IR can only access data stores and services in public networks. ⏩Self-hosted Integration Runtime: ▪️It uses infrastructure and hardware managed by you. ▪️You’ll need to address all the patching, scaling and maintenance. ▪️The IR can access resources in both public and private networks. ⏩Azure-SSIS Integration Runtime: ▪️These are VMs running the SSIS engine which allow you to natively execute SSIS packages. ▪️They are managed by Microsoft. As a result, all the patching, scaling and maintenance is taken care of. ▪️ The IR can access resources in both public and private networks ================================ For more enriching content Follow: Naman Seth 🧑💻 Share it on your feed & educate everyone.💯💪 Happy Learning!😄👐 #azure #microsoft #infrastructure #data #etl #privatecloud #hybridcloud #publiccloud #azuredataengineer #datafactory #azuredatafactory #adf #dataengineering #integrationrutime #ir #elt #datascience #datatransformation #dataanalyst

Are you aware that NoSQL databases are transforming modern applications and will soon be the go-to choice for developers? However, what makes them excel in the digital realm? #programmersforceuk #softwaredevelopment #AISolutions #nosql #developers #GenerativeAI #digital #FinTech