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Is Snowflake software part of every company's AI strategy or more comprehensively stated, part of AI Ops/Data Ops? In this week's video, we are going to discuss #Snowflake and how it relates to the overall #datapipeline and AI/Data Ops of many companies. We will briefly touch on why we are seeing so many companies out there looking for Snowflake expertise and why it has become so popular. Databricks fans don't worry, you have a separate video that is upcoming in the weeks ahead and in the video I will explain why we did not touch on Databricks in this video. Of course all of this content is really to provide a weekly nugget of knowledge from the current #AILandscape As always if you would like us to cover any other topics please be sure to leave your suggestions in the comments below. As a general rule, I try to keep the content of these videos approachable to the majority of audiences and provide some level of insights that will help fellow recruiters internal or external or prospective data talent job seekers. #DataStrategy #DataJobs #ChatGPT #AIJobs #DataManagement #DataTalent #Analytics #AiHiring #Snowflake #Cloud #CloudEDW #Teradata #YellowBrick #DBTLabs #Matillion #EDW #DataWarehouse #Hadoop #RDBMS #MachineLearning #ArtificialIntelligence #DigitalTransformation #FiveTran #Talend #Informatica #Cloudera Allie K. Miller Carla Gentry Kirk Borne, Ph.D. Bruno Aziza Steve Nouri Inderpal Bhandari David Gleason David Castillo, PhD Vincent Granville Kurt Cagle Malcolm Chisholm Ph.D. T. Scott Clendaniel Hilary Mason Doug Cutting Malcolm SmithClaudia Perlich Christina Stathopoulos, MSc Linas Beliūnas Karissa A. Breen Shekar Pannala

Is Snowflake software part of every company's AI strategy or more comprehensively stated, part of AI Ops/Data Ops? In this week's video, we are going to discuss #Snowflake and how it relates to the overall #datapipeline and AI/Data Ops of many companies. We will briefly touch on why we are seeing so many companies out there looking for Snowflake expertise and why it has become so popular. Databricks fans don't worry, you have a separate video that is upcoming in the weeks ahead and in the video, we will explain why we did not touch on Databricks in this video. Of course, all of this content is really to provide a weekly nugget of knowledge from the current #AILandscape. As always if you would like us to cover any other topics, please be sure to leave your suggestions in the comments below. As a general rule, we try to keep the content of these videos approachable to the majority of audiences and provide some level of insights that will help fellow recruiters internal or external or prospective data talent job seekers. #DataStrategy #DataJobs #ChatGPT #AIJobs #DataManagement #DataTalent #Analytics #AiHiring #Snowflake #Cloud #CloudEDW #Teradata #YellowBrick #DBTLabs #Matillion #EDW #DataWarehouse #Hadoop #RDBMS #MachineLearning #ArtificialIntelligence #DigitalTransformation #FiveTran #Talend #Informatica #Cloudera

𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝗸𝗶𝗹𝗹 𝗦𝗲𝘁𝘀 👇 Master these skills to excel as a Data Engineer. Don’t get too hung up on tools; focus on the fundamentals. 𝗖𝗼𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀: 1. 𝗦𝗤𝗟: Crucial for database querying. 2. 𝗣𝘆𝘁𝗵𝗼𝗻: Essential for data manipulation and automation. 3. 𝗘𝗧𝗟 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀: Extract, transform, load data efficiently. 4. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴: Knowledge of Redshift, BigQuery, or Snowflake. 5. 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: AWS, GCP, or Azure. 6. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Understand different data models. 7. 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗧𝗼𝗼𝗹𝘀: Hadoop, Spark for handling large datasets. 8. 𝗔𝗣𝗜𝘀: Integrate and manage data sources. 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀: 1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Clearly share ideas and findings. 2. 𝗧𝗲𝗮𝗺𝘄𝗼𝗿𝗸: Collaborate effectively with others. 3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗶𝗻𝗴: Tackle complex data challenges. 4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Learn and adapt to new technologies quickly. P.S.: Did we miss any important skills? Hit the 🔔 on my profile John K. Moran and share your thoughts in the comments.👇 #Dataanalyst #DataStrategy #TechTerms #DataEngineering #analytics #dataengineer #data

🎯CRACKING AZURE DATA ENGINEERING JOB 🔥 𝐂𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐉𝐨𝐛 𝐢𝐧 100 𝐝𝐚𝐲𝐬- 𝐈𝐅 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭, (𝐒𝐚𝐥𝐚𝐫𝐲 𝐑𝐚𝐧𝐠𝐞 10 𝐋𝐏𝐀 𝐭𝐨 30 𝐋𝐏𝐀 𝐦𝐢𝐧 𝐁𝐚𝐬𝐞𝐝 𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞) : https://lnkd.in/g-UBrdiZ  #dataengineering #linkedin #databricks #sql #dataengineer #dataanalytics #spark #pyspark #practice #engineer #bigdata

🎯 Databricks Performance Tuning … Follow Devikrishna R 🇮🇳 💎 for more contents☺️ 🔴Azure data engineer 1*1 support:  🔴Success Stories : https://lnkd.in/g6_7Q7mK 🔴Third Group : https://lnkd.in/gP9Qa47K #azure #interview #dataengineering #databricks #interview

#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 [email protected] 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 [email protected] 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.

Please share resume at [email protected] Title: Azure Databricks Architect with PySpark and Snowflake exp Location: NYC, NY - Onsite JD: Exp level : 15 years Hands-on experience with Azure Databricks platform. Extensive experience in PySpark coding Design, develop, and deploy Databricks jobs to process and analyze large volumes of data. Collaborate with data engineers and data scientists to understand data requirements and implement appropriate data processing pipelines. Optimize Databricks jobs for performance and scalability to handle big data workloads. Monitor and troubleshoot Databricks jobs, identify and resolve issues or bottlenecks. Implement best practices for data management, security, and governance within the Databricks environment #dataarchitect #AzureArchitect #Architect #Databricks #DatabricksArchitect

#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 [email protected] 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.

If you are thinking of giving an interview for the Data Engineer role. Things you should know and learn in a higher view. ✅ 𝐂𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Data Modelling, ETL/ELT Processes and Data Warehousing ✅ 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: SQL & Python ✅ 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: Apache Spark, Apache Flink, and Kafka. ✅ 𝐂𝐥𝐨𝐮𝐝 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬: AWS, GCP, or Azure, focusing on their data engineering offerings ✅ 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Understanding Hadoop, Hive, HDFS, etc because that is the core ✅ 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Know various design patterns for data ingestion, processing, and storage ✅ 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠: Query optimization, indexing strategies, and how to optimize ETL jobs  ✅ 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧: Airflow Please let me know if I missed anything in the comment section. 𝑾𝒂𝒏𝒕𝒆𝒅 𝒕𝒐 𝒄𝒐𝒏𝒏𝒆𝒄𝒕 𝒘𝒊𝒕𝒉 𝒎𝒆 𝒐𝒏 𝒂𝒏𝒚 𝒕𝒐𝒑𝒊𝒄𝒔, 𝒇𝒊𝒏𝒅 𝒎𝒆 𝒉𝒆𝒓𝒆 --> https://lnkd.in/gbv9aT36 👉𝐅𝐨𝐥𝐥𝐨𝐰 Nizamuddin M 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐬𝐮𝐜𝐡 𝐜𝐨𝐧𝐭𝐞𝐧𝐭. #dataengineering #bigdata #interview #opentowork #cfbr #big4 #dataanalytics #Pysparkinterview #Pyspark #dataengineer #grow