Adam Howell’s Post

#AWS #Databricks #Developer #ContractRole #DataEngineering #DataProcessing #DataPipeline #DataManagement #ETL #BigData #Analytics #CloudComputing #DataLake #DataWarehouse #PySpark #SQL #NoSQL #DataIntegration #DataScience #DataAnalytics #AWSDeveloper #TechJobs #ITContracts #RemoteWork #JobOpportunity #Databricks #DataEngineering #DataJobs #AWSJobs #BigData #PySpark #DataPipeline #DataProcessing #DataAnalytics #ETL #DataScience #CloudComputing #DataWarehouse #TechJobs #JobOpportunity #ContractRole #RemoteWork #TechCareers please share resume with hr-ops@pinakin-kantha.com Seeking an individual available to provide support post 6 PM for a duration of 4 hours. Please consider the following prerequisites: Extensive experience in AWS Databricks development and administration. Databricks certification. Proficiency in Metastore, Unity Catalogue, and Account Console User Provisioning. Responsibilities: Design, execute, and manage data processing pipelines using Databricks. Administer and oversee Databricks clusters to ensure optimal performance, reliability, and security. Collaborate with diverse teams to deploy, configure, and supervise Databricks environments. Enforce security protocols for Databricks workspaces, including access controls and data encryption. Monitor system health, address issues, and conduct routine maintenance activities. Coordinate with data engineers and scientists to optimize cluster configurations based on workload requirements. Integrate Databricks with enterprise systems in collaboration with IT teams. Stay informed about Databricks updates, patches, and features, implementing them as necessary. Offer technical support and mentorship to junior administrators. Develop and manage ETL processes to ensure data accuracy and consistency. Collaborate with business stakeholders to translate analytics requirements into Databricks workflows. Implement and manage version control for Databricks notebooks and code. Troubleshoot and resolve issues with Databricks jobs, addressing performance concerns. Stay current with Databricks best practices and incorporate them into development methodologies.

#AWS #Databricks #Developer #ContractRole #DataEngineering #DataProcessing #DataPipeline #DataManagement #ETL #BigData #Analytics #CloudComputing #DataLake #DataWarehouse #PySpark #SQL #NoSQL #DataIntegration #DataScience #DataAnalytics #AWSDeveloper #TechJobs #ITContracts #RemoteWork #JobOpportunity #Databricks #DataEngineering #DataJobs #AWSJobs #BigData #PySpark #DataPipeline #DataProcessing #DataAnalytics #ETL #DataScience #CloudComputing #DataWarehouse #TechJobs #JobOpportunity #ContractRole #RemoteWork #TechCareers please share resume with hr-ops@pinakin-kantha.com Seeking an individual available to provide support post 6 PM for a duration of 4 hours. Please consider the following prerequisites: Extensive experience in AWS Databricks development and administration. Databricks certification. Proficiency in Metastore, Unity Catalogue, and Account Console User Provisioning. Responsibilities: Design, execute, and manage data processing pipelines using Databricks. Administer and oversee Databricks clusters to ensure optimal performance, reliability, and security. Collaborate with diverse teams to deploy, configure, and supervise Databricks environments. Enforce security protocols for Databricks workspaces, including access controls and data encryption. Monitor system health, address issues, and conduct routine maintenance activities. Coordinate with data engineers and scientists to optimize cluster configurations based on workload requirements. Integrate Databricks with enterprise systems in collaboration with IT teams. Stay informed about Databricks updates, patches, and features, implementing them as necessary. Offer technical support and mentorship to junior administrators. Develop and manage ETL processes to ensure data accuracy and consistency. Collaborate with business stakeholders to translate analytics requirements into Databricks workflows. Implement and manage version control for Databricks notebooks and code. Troubleshoot and resolve issues with Databricks jobs, addressing performance concerns. Stay current with Databricks best practices and incorporate them into development methodologies.

#AWS #Databricks #Developer #ContractRole #DataEngineering #DataProcessing #DataPipeline #DataManagement #ETL #BigData #Analytics #CloudComputing #DataLake #DataWarehouse #PySpark #SQL #NoSQL #DataIntegration #DataScience #DataAnalytics #AWSDeveloper #TechJobs #ITContracts #RemoteWork #JobOpportunity #Databricks #DataEngineering #DataJobs #AWSJobs #BigData #PySpark #DataPipeline #DataProcessing #DataAnalytics #ETL #DataScience #CloudComputing #DataWarehouse #TechJobs #JobOpportunity #ContractRole #RemoteWork #TechCareers please share resume with hr-ops@pinakin-kantha.com Seeking an individual available to provide support post 6 PM for a duration of 4 hours. Please consider the following prerequisites: Extensive experience in AWS Databricks development and administration. Databricks certification. Proficiency in Metastore, Unity Catalogue, and Account Console User Provisioning. Responsibilities: Design, execute, and manage data processing pipelines using Databricks. Administer and oversee Databricks clusters to ensure optimal performance, reliability, and security. Collaborate with diverse teams to deploy, configure, and supervise Databricks environments. Enforce security protocols for Databricks workspaces, including access controls and data encryption. Monitor system health, address issues, and conduct routine maintenance activities. Coordinate with data engineers and scientists to optimize cluster configurations based on workload requirements. Integrate Databricks with enterprise systems in collaboration with IT teams. Stay informed about Databricks updates, patches, and features, implementing them as necessary. Offer technical support and mentorship to junior administrators. Develop and manage ETL processes to ensure data accuracy and consistency. Collaborate with business stakeholders to translate analytics requirements into Databricks workflows. Implement and manage version control for Databricks notebooks and code. Troubleshoot and resolve issues with Databricks jobs, addressing performance concerns. Stay current with Databricks best practices and incorporate them into development methodologies.

🎯 𝐃𝐚𝐲 1: 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐃𝐞𝐞𝐩 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠  ✅ 𝐇𝐨𝐰 𝐭𝐨 𝐝𝐞𝐭𝐞𝐫𝐦𝐢𝐧𝐞 𝐚𝐳𝐮𝐫𝐞 𝐝𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐜𝐨𝐦𝐩𝐮𝐭𝐞 𝐜𝐨𝐧𝐟𝐮𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧? 𝐡𝐨𝐰 𝐭𝐨 𝐬𝐚𝐯𝐞 𝐜𝐨𝐬𝐭 𝐞𝐟𝐟𝐢𝐞𝐧𝐜𝐭𝐥𝐲? Azure Databricks compute encompasses the array of computing resources available within the Azure Databricks workspace, vital for executing diverse workloads spanning data engineering, data science, and data analytics. These tasks encompass production ETL pipelines, streaming analytics, ad-hoc analysis, and machine learning endeavors. Users possess the flexibility to leverage existing compute resources or create new ones, contingent upon their permissions. The Compute section within the workspace provides visibility into accessible compute resources.: The types of compute available in Azure Databricks include: 👉All-Purpose Compute: Pre-provisioned compute infrastructure utilized for data analysis within notebooks. Users can initiate, terminate, and restart this compute via the UI, CLI, or REST API. 👉Job Compute: Dedicated compute provisioned for automated job execution. The Azure Databricks job scheduler dynamically creates job compute instances as required, terminating them upon job completion. Restarting job compute instances isn't supported. 👉Instance Pools: Compute resources housing idle, pre-configured instances, aimed at reducing start-up and autoscaling durations. Users can instantiate instance pools through the UI, CLI, or REST API. 👉Serverless SQL Warehouses: Elastic, on-demand compute resources facilitating SQL command execution on data objects within the SQL editor or interactive notebooks. Users can create SQL warehouses via the UI, CLI, or REST API. 👉Classic SQL Warehouses: Compute resources dedicated to executing SQL commands on data objects within the SQL editor or interactive notebooks. Similar to serverless SQL warehouses, creation is possible through the UI, CLI, or REST API. Articles within this section elucidate procedures for managing compute resources via the Azure Databricks UI. For alternative approaches, consult resources detailing command-line usage and referencing the Databricks REST API. Follow Devikrishna R 🇮🇳 💎 for more contents☺️ 🔴Azure data engineer 1*1 support:  🔴Third Group : https://lnkd.in/gP9Qa47K 🔴First Group: https://lnkd.in/g-ScPYr4 🔴Second Group : https://lnkd.in/giHGDVRX #azure #interview #dataengineering #databricks #interview  #pyspark #coding #azuredatabricks

Hey Network, 🤑 This post is a bit out of my scope but we are looking for a highly qualified data engineer. Must have GCP and Databricks experience, please share with your network, repost, like and interact with this post. If you think you're a match for this role please reach out to myself or the email found at the bottom of this post. #data #dataengineer #GCP #Databricks

🚀 Choosing Between Open-Source Tools and Fully Managed Services in Data Engineering I recently came across an interesting question during an interview that got me thinking: "When should you choose open-source tools over fully managed services in data engineering?" It’s a decision I’ve had to make a few times in my career, and here’s how I approach it: 🔧 Open-Source Tools (like Spark, Kafka, Airflow) are my go-to when:   - Customization is key. When I need full control over the architecture, or I have unique requirements that demand tweaking.   - It’s a smaller-scale project where the operational costs can be kept low, and you want to avoid vendor lock-in. 🔌 Fully Managed Services (like Azure Synapse, Databricks) are a lifesaver when:   - Scalability is crucial. You don’t want to worry about infrastructure—just let the platform handle it.   - The focus is on speed and delivering results. You can spend more time on building data insights and less time on managing the backend.   - There’s a need for security and compliance, especially in larger organizations where those concerns are non-negotiable. At the end of the day, it’s all about finding the balance between control and efficiency. Sometimes, the right choice isn't about which tool is more powerful, but which one gets you to your goal faster and more effectively. #DataEngineering #BigData #CloudComputing #Spark #Azure #AWS #DataOps #ETL #DataAnalytics #EuropeTech #UAEJobs #QatarJobs #GermanyTech #IndiaDataCommunity #Sumitteaches #trendytech #DataInterviewPrep

Over the past 9+ years, I've had the privilege of working across various data engineering and cloud platforms, helping organizations streamline their data processes and unlock the true potential of their data. From building robust ETL pipelines to migrating massive datasets to the cloud, I’ve been passionate about delivering data-driven solutions that drive results.My expertise includes: --Extensive experience in AWS, Azure, and Snowflake for cloud-based data engineering --Expertise in ETL processes, data warehousing, and data pipeline optimization Proficient in Python, SQL, and various other programming languages for data manipulation --Hands-on experience managing and migrating petabytes of data across global regions --Proven track record of leading projects from the ground up, including full-scale cloud migrations and big data architectures --What drives me is the ability to solve complex data challenges and architect scalable solutions that empower businesses to leverage their data assets effectively. I’m constantly looking for new ways to innovate in the fields of big data and cloud technologies. --If you're passionate about data, cloud, or simply want to chat about the future of data engineering, feel free to connect or drop me a message! Let's push the boundaries of what's possible in the world of data together. #DataEngineering #CloudSolutions #BigData #AWS #Azure #Snowflake #ETL #CloudMigration #DataArchitect

As somebody who is well immersed in the data & analytics market I often get asked by candidates of all levels where they should be building their skills❓ There are the tech skills such as Azure, Databricks, AWS, Snowflake, Fabric etc but as far as broader skillsets are concerned I have seen a recent uptick in two key areas. 📈 💥Data Ops 💥Data Governance I will be releasing an article on Monday going into detail about what both mean and why these two areas are key focusses for clients going into 2024. 👀 #datagovernance #dataops #jobmarkettrends #dataanalytics

Hello Connections 🙋♂️ I've launched a YouTube video series focused on interview preparation for most used GCP Data Engineering services. ☁ This is the second video of this series and I have discussed most commonly faced interview questions about the two very important GCP Data Engineering ETL Tools : Dataflow (Apache Beam) and Cloud Functions. The questions are organized by difficulty—easy, medium, and hard—so you can follow along at your own pace. 💫 Check it out if you're preparing for interviews or want to strengthen your knowledge in these areas! Also let me know in the comment section if I have missed any topics. #Dataflow #ApacheBeam #interview #Beam #Preparation #GCP #GoogleCloud #dataengineeringessentials #bigdata #interviewtips #interviewquestions #etl #elt #cloudfunctions #functions #serverlesscomputing