MDOS Consulting’s Post

𝗗𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗡𝗼𝘁𝗶𝗼𝗻𝘀 : ❗ One of the most important aspects of data engineering is the security and access control of your data. When working on a data engineering project, collaboration within a team is common, making it crucial to define access levels for everyone to prevent any potential access issues. For instance, in my previous post (https://lnkd.in/e5vdhV2e), where I outlined the data pipeline of a current project, you can restrict access to S3 buckets, ensuring that only administrators have access. ↘ 𝗧𝗵𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗵𝗲𝗹𝗽𝘀 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁𝗮𝗹 𝗱𝗲𝗹𝗲𝘁𝗶𝗼𝗻 𝗲𝗿𝗿𝗼𝗿𝘀 𝗰𝗮𝘂𝘀𝗲𝗱 𝗯𝘆 𝗶𝗻𝘃𝗼𝗹𝘂𝗻𝘁𝗮𝗿𝘆 𝗮𝗰𝘁𝗶𝗼𝗻𝘀. 🙂 To achieve this, AWS and other cloud providers have implemented identity management tools. IAM (Identity and Access Management) assists in: 1. 𝗠𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗰𝗰𝗲𝘀𝘀 : IAM allows you to define who has access to which AWS services and resources. Each IAM user possesses unique access credentials (username and password or access key) for logging into their AWS account. 2. 𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗮𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝘂𝘀𝗲𝗿𝘀 : You can associate IAM policies with users to specify permissions. These policies outline what actions IAM users are permitted to perform. 3. 𝗔𝘂𝗱𝗶𝘁 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 : IAM offers audit features for tracking actions carried out by IAM users, facilitating compliance with security policies and regulatory requirements. 4. 𝗞𝗲𝘆 𝗥𝗼𝘁𝗮𝘁𝗶𝗼𝗻 : For users with access keys, IAM allows regular rotation of these keys to enhance security. 🤝 𝗦𝗼, 𝗜 𝗲𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘆𝗼𝘂 𝘁𝗼 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 𝘂𝘀𝗲 𝘆𝗼𝘂𝗿 𝗜𝗔𝗠 , 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗲𝗻𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. Thank you for following, and don't hesitate to share it with your data colleagues. See you ! 🙂 Arthur #DataEngineering #DataEngineeringwitharthur #database #datawarehouse #storage #Redshift #IAM #AWS

𝐇𝐨𝐰 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐚𝐧𝐲 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲? Here is a master template that I used to discuss many hashtag #systemdesign problems in hashtag #interviews. 1. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫: Distributes incoming network traffic across multiple servers to ensure no single server bears too much demand. This helps increase the availability and reliability of applications. 2. 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲: Acts as a gatekeeper for APIs, handling request routing, composition, and protocol translation. Often includes functionalities like authentication, monitoring, and load balancing. 3. 𝐒𝐭𝐚𝐭𝐢𝐜 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 & 𝐂𝐃𝐍: Delivery of static assets (like images and scripts) that don't change often, using a Content Delivery Network (CDN) to speed up their delivery by caching content in multiple locations around the world. 4. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐒𝐞𝐫𝐯𝐞𝐫 𝐚𝐧𝐝 𝐁𝐥𝐨𝐜𝐤 𝐒𝐞𝐫𝐯𝐞𝐫: Components that manage metadata and data blocks, respectively. Metadata servers store information about where data blocks are located and their properties, while block servers actually store the data chunks. 5. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐅𝐢𝐥𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Storage system designed to store data across multiple points of presence to increase data redundancy and reliability. 6. 𝐅𝐞𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Handles the processing and queuing of data needed to generate user-specific content feeds, like those seen on social media platforms. 7. 𝐒𝐡𝐚𝐫𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 & 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐲 𝐁𝐚𝐬𝐞𝐝 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Manages data sharding and partitioning strategies, optimizing data distribution and access by dividing larger databases into more manageable pieces. 8. 𝐍𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Manages sending notifications to users, handling both the queueing of notification messages and their delivery. 9. 𝐂𝐚𝐜𝐡𝐞 (𝐑𝐞𝐝𝐢𝐬/𝐌𝐞𝐦𝐜𝐚𝐜𝐡𝐞𝐝): In-memory data storage used to reduce the number of times data is read from slower databases, enhancing performance by serving repeated requests for the same data quickly. 10. 𝐕𝐢𝐝𝐞𝐨 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐐𝐮𝐞𝐮𝐞 & 𝐖𝐨𝐫𝐤𝐞𝐫𝐬: Dedicated system components for video processing tasks, queuing them and distributing the workload among multiple workers to process the videos as needed. 11. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐜𝐢𝐧𝐠: Systems for logging and tracing the behavior of distributed systems, essential for debugging and monitoring the performance of distributed applications. 12. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐇𝐚𝐝𝐨𝐨𝐩/𝐌𝐚𝐩𝐑𝐞𝐝𝐮𝐜𝐞, 𝐒𝐩𝐚𝐫𝐤): Frameworks used for processing large data sets with a distributed algorithm on a cluster. Ref: ✅𝗚𝗿𝗼𝗸𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 - https://lnkd.in/gkYKjGAp

𝐇𝐨𝐰 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐚𝐧𝐲 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲? Here is a master template that I used to discuss many #systemdesign problems in #interviews. 1. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫: Distributes incoming network traffic across multiple servers to ensure no single server bears too much demand. This helps increase the availability and reliability of applications. 2. 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲: Acts as a gatekeeper for APIs, handling request routing, composition, and protocol translation. Often includes functionalities like authentication, monitoring, and load balancing. 3. 𝐒𝐭𝐚𝐭𝐢𝐜 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 & 𝐂𝐃𝐍: Delivery of static assets (like images and scripts) that don't change often, using a Content Delivery Network (CDN) to speed up their delivery by caching content in multiple locations around the world. 4. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐒𝐞𝐫𝐯𝐞𝐫 𝐚𝐧𝐝 𝐁𝐥𝐨𝐜𝐤 𝐒𝐞𝐫𝐯𝐞𝐫: Components that manage metadata and data blocks, respectively. Metadata servers store information about where data blocks are located and their properties, while block servers actually store the data chunks. 5. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐅𝐢𝐥𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Storage system designed to store data across multiple points of presence to increase data redundancy and reliability. 6. 𝐅𝐞𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Handles the processing and queuing of data needed to generate user-specific content feeds, like those seen on social media platforms. 7. 𝐒𝐡𝐚𝐫𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 & 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐲 𝐁𝐚𝐬𝐞𝐝 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Manages data sharding and partitioning strategies, optimizing data distribution and access by dividing larger databases into more manageable pieces. 8. 𝐍𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Manages sending notifications to users, handling both the queueing of notification messages and their delivery. 9. 𝐂𝐚𝐜𝐡𝐞 (𝐑𝐞𝐝𝐢𝐬/𝐌𝐞𝐦𝐜𝐚𝐜𝐡𝐞𝐝): In-memory data storage used to reduce the number of times data is read from slower databases, enhancing performance by serving repeated requests for the same data quickly. 10. 𝐕𝐢𝐝𝐞𝐨 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐐𝐮𝐞𝐮𝐞 & 𝐖𝐨𝐫𝐤𝐞𝐫𝐬: Dedicated system components for video processing tasks, queuing them and distributing the workload among multiple workers to process the videos as needed. 11. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐜𝐢𝐧𝐠: Systems for logging and tracing the behavior of distributed systems, essential for debugging and monitoring the performance of distributed applications. 12. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐇𝐚𝐝𝐨𝐨𝐩/𝐌𝐚𝐩𝐑𝐞𝐝𝐮𝐜𝐞, 𝐒𝐩𝐚𝐫𝐤): Frameworks used for processing large data sets with a distributed algorithm on a cluster. Ref: ✅𝗚𝗿𝗼𝗸𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 - https://lnkd.in/g4Wii9r7 Follow Tauseef Fayyaz for more helpful content! #coding #software #programming

𝐇𝐨𝐰 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫 𝐚𝐧𝐲 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲? Here is a master template that I used to discuss many #systemdesign problems in #interviews. 1. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫: Distributes incoming network traffic across multiple servers to ensure no single server bears too much demand. This helps increase the availability and reliability of applications. 2. 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲: Acts as a gatekeeper for APIs, handling request routing, composition, and protocol translation. Often includes functionalities like authentication, monitoring, and load balancing. 3. 𝐒𝐭𝐚𝐭𝐢𝐜 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 & 𝐂𝐃𝐍: Delivery of static assets (like images and scripts) that don't change often, using a Content Delivery Network (CDN) to speed up their delivery by caching content in multiple locations around the world. 4. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐒𝐞𝐫𝐯𝐞𝐫 𝐚𝐧𝐝 𝐁𝐥𝐨𝐜𝐤 𝐒𝐞𝐫𝐯𝐞𝐫: Components that manage metadata and data blocks, respectively. Metadata servers store information about where data blocks are located and their properties, while block servers actually store the data chunks. 5. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐅𝐢𝐥𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Storage system designed to store data across multiple points of presence to increase data redundancy and reliability. 6. 𝐅𝐞𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Handles the processing and queuing of data needed to generate user-specific content feeds, like those seen on social media platforms. 7. 𝐒𝐡𝐚𝐫𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 & 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐲 𝐁𝐚𝐬𝐞𝐝 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Manages data sharding and partitioning strategies, optimizing data distribution and access by dividing larger databases into more manageable pieces. 8. 𝐍𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 & 𝐐𝐮𝐞𝐮𝐞: Manages sending notifications to users, handling both the queueing of notification messages and their delivery. 9. 𝐂𝐚𝐜𝐡𝐞 (𝐑𝐞𝐝𝐢𝐬/𝐌𝐞𝐦𝐜𝐚𝐜𝐡𝐞𝐝): In-memory data storage used to reduce the number of times data is read from slower databases, enhancing performance by serving repeated requests for the same data quickly. 10. 𝐕𝐢𝐝𝐞𝐨 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐐𝐮𝐞𝐮𝐞 & 𝐖𝐨𝐫𝐤𝐞𝐫𝐬: Dedicated system components for video processing tasks, queuing them and distributing the workload among multiple workers to process the videos as needed. 11. 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐜𝐢𝐧𝐠: Systems for logging and tracing the behavior of distributed systems, essential for debugging and monitoring the performance of distributed applications. 12. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (𝐇𝐚𝐝𝐨𝐨𝐩/𝐌𝐚𝐩𝐑𝐞𝐝𝐮𝐜𝐞, 𝐒𝐩𝐚𝐫𝐤): Frameworks used for processing large data sets with a distributed algorithm on a cluster. Ref: ✅𝗚𝗿𝗼𝗸𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 - https://lnkd.in/g4Wii9r7 📌 Recommend DesignGurus.io's courses to earn 20% on each referral: https://lnkd.in/gz4rqr5k

implementation strategy is another key to this

How do you identify a good database and infrastructure management team which supports analytics? Efficient database infrastructure management is crucial for any organization that relies on digital data storage and retrieval. A database infrastructure manager/team plays a vital role in ensuring that a company's database systems are well-maintained, secure and operating at peak performance. Some result driven traits to look for - 🧑💻Designs and implements a robust database architecture that meets the organization's needs. This includes selecting the appropriate database management system (DBMS), designing data models and optimising database performance to process large volumes of data. 📈 Must ensure that the database infrastructure scales effectively as the organization grows and that data is easily accessible to authorized users. 🛠️Fine tunes performance by optimizing database queries, indexing and storage to ensure that data can be retrieved quickly and efficiently. 👨👨👦👦Works closely with other IT teams, developers and business stakeholders to understand the organization's data requirements and ensure that the database infrastructure meets those needs. 🆘Puts in place strong security measures to keep critical data safe from unwanted access, theft, or corruption. This includes configuring user permissions, encryption and access controls, as well as periodically monitoring and inspecting the database for any security threats. Overall, a good database infrastructure management team plays a crucial role in ensuring that an organization's database systems are secure, efficient and capable of supporting the organization's data needs and therefore, help drive business success and ensure that data remains a valuable asset for the organization. #machinelearning #engineering #database #security #management #leadership

Hi Linkies!! We are hiring for Production Support Engineer in our organization. About CES Ltd. : CES is a leading global provider of Technology and Business Process Modernization (BPM) services for enterprise clients. The Company is widely recognized as a trusted partner to deliver complex technology solutions and modernize mission critical business processes. To further support its value proposition and drive its recurring revenue profile, CES has developed a suite of proprietary IP to differentiate its service offering including a cloud-based web harvesting platform, UI & API test automation, a cash reconciliation platform, and a cloud-enabled workflow management tool. Led by a seasoned leadership team based in the United States, the Company has a global delivery platform with highly specialized consultants, both onshore and offshore across three continents. Job Title: Production Support Engineer Location: Hyderabad Experience: 7+ years Work Timings: 4:30 AM - 1:30 PM and 7:30 PM - 4:30 AM Position Overview: The Production Support Consultant is a critical role centered on maintaining the robustness and efficiency of our data processing environment. This position requires a hands-on approach to triaging production issues, diving deep into data problems, and refining/optimizing our data pipelines. Responsibilities: • Serve as the first line of defense for production issues, swiftly triaging and prioritizing incidents to ensure minimal disruption to business operations. • Collaborate with the data engineering team to streamline and optimize data pipelines, ensuring efficient data flow and quality across the organization. • Configure and optimize production vendor data sourcing jobs, ensuring they run reliably and efficiently, and address any scheduling conflicts or failures promptly. • Provide timely support to investment researchers for data-related queries and challenges. • Engage closely with internal teams on system upgrades, data implementations, and incident post-mortems to prevent future issues. • Review and refine current operational processes, introducing tools or methods that can reduce incident recurrence and improve response times. • Ability to work in a fast-paced environment and be able to work on multiple projects simultaneously. Requirements: • A degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent experience. • Experience (3+ years) in triaging and resolving production-level issues in a timely manner. • Proficiency in Python, especially in the context of data operations and pipeline optimizations. • Practical experience in SQL querying, with the ability to efficiently diagnose and rectify data anomalies. • Familiarity with enterprise technology tools: Linux, SQL Server, GIT. • Finance/market data experience: familiarity with vendors such as Bloomberg, Refinitiv, Nasdaq and others is a must. Please share your updated profile at praniti.pandya@cesltd.com. Regards, Praniti

Maintaining a healthy and robust database is crucial for any organization that relies on data for its critical business operations. Here are ten tips that can help you keep your database healthy: 1. Regular Backups: Implement a backup strategy that is reliable and robust to ensure data protection and disaster recovery. You should schedule regular backups and store them securely offsite to mitigate the risk of data loss. 2. Monitor Performance: Continuously monitor database performance metrics such as CPU usage, memory utilization, and query execution times. Identifying and addressing performance bottlenecks proactively can help you maintain optimal database performance. 3. Patch Management: Keep your database software up to date by applying patches and updates regularly. This will help you address security vulnerabilities, bug fixes, and performance improvements. 4. Optimize Queries: Optimize your database queries to improve performance and reduce resource consumption. Use indexes, query tuning, and optimization techniques to optimize query execution times. 5. Data Validation: Implement data validation checks to ensure data integrity and consistency. Validate input data against predefined rules and constraints to prevent data corruption and maintain data quality. 6. Security Measures: Implement robust security measures to protect sensitive data from unauthorized access and cyber threats. Utilize encryption, access controls, and security best practices to safeguard your database assets. 7. Regular Maintenance: Perform routine database maintenance tasks such as index rebuilds, data compaction, and statistics updates. Regular maintenance helps prevent database fragmentation and optimize storage utilization. 8. Capacity Planning: Conduct regular capacity planning assessments to anticipate future growth and resource requirements. Scale your database resources accordingly to accommodate increasing data volumes and user loads. 9. Documentation: Maintain comprehensive documentation of your database configurations, schemas, and procedures. Documentation facilitates troubleshooting, knowledge transfer, and compliance with regulatory requirements. 10. Disaster Recovery Planning: Develop a disaster recovery plan that is robust enough to minimize downtime and data loss in the event of a disaster. Test your disaster recovery procedures regularly to ensure readiness and effectiveness. By following these top 10 tips, organizations can effectively maintain a healthy database environment, ensuring data integrity, performance, and availability for critical business operations. #CroyantTechnologies - Database management solutions provider #databasemanagement #oracledba #databasesolutions #mongodb

Archive Storage Solution 🗃️ Ideal for structured and unstructured #data, such as financial reports, emails, web content, log data, historical data, retired application data, database records, and spreadsheets. Primary storage - High IOPS level for r/w activities. Archive storage - low performance, high capacity storage mediums, Long term data retention but may only access occasionally.  WORM functionality ( read-only ) core features of archive storage solutions: - Searchability - Data deduplication - Data lifecycle management - Access control - Flexibility Archive data storage solutions use different mediums, including tapes, hard drives, cloud, disks, and flash storage. Traditionally, archival systems have been file-based but now use object storage. On-premises archive storage tools use a mix of object storage solutions, tape storage, and disk storage. Tape storage systems use magnetic tapes to record and store data. ability to protect data from malware by working offline. Disk drives are electro-mechanical devices that use magnetism for data storage and retrieval. Users can also integrate them with indexing engines to search archived items faster.  What are the benefits of archive storage solutions? - Reduced storage cost - Meeting regulatory compliance - Prevention of data loss : protect sensitive information from malware infection, cyberattacks, unauthorized access, and breaches. - Easy audit tracking : visibility over who is accessing and modifying what and when - High durability : ensure data integrity during long-term preservation with redundancy and data replication features - Business agility : make it easier for businesses to retrieve, extract, and push data to production. Backup and recovery teams combine archiving and disaster recovery solutions for storing data backups and protecting critical data for an extended period.  The cost of archiving storage solutions depends on factors like: - Amount and type of data  - Desired data retrieval time - Security and compliance requirements Data archiving solutions use one of the three cost models below. - Linear model : on a per gigabyte (GB) basis for the data they store. - Asymmetric model : The cost of storing and retrieving data differs in this model. Cloud-based archive storage solutions use this pricing model. - Symmetric model : The cost of storing and retrieving data is the same in this model. On-premises archive storage solution providers use this pricing model. archived data is for retention purposes and may not be updated frequently. Identify business needs and priorities. - start with business impact analysis to understand resources they want to archive When should you implement archive storage solutions? - retain historical data or backups for an extended period #business #Archive #Storage #solutions #rvman #recovery #iops #DataProtection #Backup #Vault