Dagster Labs’ Post

Data reconciliation is a crucial aspect of data management that goes beyond simple record matching. It involves various processes to ensure data quality, consistency, and compliance. These include data quality assessment, transformation and standardization, error detection and correction, metadata management, and continuous monitoring. Furthermore, data reconciliation algorithms, data integration, and aggregation are also crucial components. By covering all these aspects, organizations can ensure the accuracy, completeness, and consistency of their data. #datareconciliation #dataquality #dataconsistency #datacompliance

Data Classification and Governance in real life: How T-Systems reacted as they were surprised from massive personal and sensitive data found on a customers environment. At NetApp Insight, Tom Cody from T-Systems told the audience about his experience within a data assess and reorganization project at a healthcare customer: "Our expectations with stale data and duplication of the data was pretty much confirmed - there was a LOT of stale data, there was a LOT of duplicate files within the environment. What we did not expect were MILLIONS of files that contain personal and sensitive information (...) and open permissions for those files." If you are interested in the whole story, please see that 18-min-Video from NetApp Insight to get a deeper impression (Login required). And of course - NetApp can help you to gain data insights in your own projects, like migration-, reorg-, outcarve-, buyout-, governance- or compliance-projects.

𝗢𝗯𝗷𝗲𝗰𝘁 𝗮𝗻𝗱 𝗙𝗶𝗹𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗶𝗻 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 🗃️ 𝗢𝗯𝗷𝗲𝗰𝘁 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝘁 𝗶𝘁𝘀 𝗰𝗼𝗿𝗲: Object storage is purpose-built to handle massive amounts of data, effortlessly scaling to petabytes and beyond, ensuring seamless growth. ✅ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗱𝘂𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Employs advanced data replication and redundancy techniques, guaranteeing high data durability and resilience against failures. ✅ 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲: By eliminating the need for expensive data migration or hierarchical storage management, object storage brings significant cost savings in the long run. ✅ Versatile: Object storage accommodates a wide range of data types, including unstructured, semi-structured, and structured data, providing unmatched flexibility for various use cases. 🚩 Risks: ❌ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗰𝘂𝗿𝘃𝗲: Adopting and configuring object storage systems may require a slight learning curve, particularly for organisations new to this technology. However, the benefits outweigh the initial investment in terms of long-term efficiency. ❌ 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝘀𝗲𝗮𝗿𝗰𝗵𝗶𝗻𝗴: Unlike file storage, object storage may not excel in granular metadata searching, impacting specific use cases. However, innovative approaches can overcome these limitations. 📂 𝗙𝗶𝗹𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: ✅ 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘆𝗲𝘁 𝗹𝗶𝗺𝗶𝘁𝗲𝗱: Offers a familiar directory and folder structure, facilitating straightforward data organisation and access. ✅ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝗰𝗲: File storage seamlessly integrates with existing applications, making it a reliable choice for organisations reliant on legacy systems. ✅ 𝗚𝗿𝗮𝗻𝘂𝗹𝗮𝗿 𝗰𝗼𝗻𝘁𝗿𝗼𝗹: Empowers precise permission settings and security measures on individual files, enhancing data protection. ✅ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀: File storage excels at handling small to medium-sized files, providing low-latency access, and ensuring optimised performance in specific scenarios. 🚩 Risks: ❌ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗹𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀: As the number of files grows, managing file storage can become challenging, potentially leading to performance bottlenecks. However, innovative solutions can mitigate these challenges. ❌ 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝘀𝗵𝗮𝗿𝗶𝗻𝗴: Sharing large amounts of data across multiple users or organisations may require additional tools or workarounds, introducing complexities and potential inefficiencies. ❌ 𝗛𝗶𝗴𝗵𝗲𝗿 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱: File storage incurs additional overhead due to file metadata and data management operations, impacting resource utilization. #DataManagement #Efficiency #ObjectStorage #FileStorage

Metadata management (MDM) represents a holistic approach encompassing the entirety of a data's lifecycle, transcending mere data cleanliness and accuracy. It entails meticulous oversight from data inception to retirement, ensuring that data, irrespective of its origin or structure, maintains accuracy, accessibility, and applicability at the moment of need. This entails: Accuracy Assurance: Vigilantly upholding data integrity by implementing stringent quality standards, conducting thorough validations, and promptly addressing any discrepancies or inaccuracies. Availability Guarantee: Facilitating seamless data accessibility for authorized users through meticulous management of storage infrastructure, replication mechanisms, and distribution channels, thereby ensuring uninterrupted access to critical information. Actionability Enhancement: Enriching data with pertinent metadata attributes, including descriptive tags and contextual annotations, to facilitate effortless searchability, comprehension, and utilization, thus empowering stakeholders to derive actionable insights and informed decisions.

What is Data Deduplication? Data deduplication is a technique that minimizes the space required to store data. It is designed to help organizations address the issue of duplicate data. Whether a company accumulates multiple copies of the same exact file or multiple files containing the same data, deduplication replaces extra copies of data with metadata that simply points back to the original. Why do we need data deduplication? Data deduplication helps IT departments reduce not only storage space requirements, but also the costs associated with duplicated data. Large datasets often have lots of duplication, increasing storage costs. The space savings gained from data deduplication depends on the dataset or workload on the volume. Datasets with high duplication could achieve optimization rates of up to 95%. Data duplication also helps reduce the amount of bandwidth wasted on transferring data to and from remote storage locations. And the ability to effectively manage storage resources can make all the difference to your backup capabilities: · Efficient storage allocation · Cost savings · Network optimization · Data center efficiency · Fast recovery and continuity HPE and data deduplication Not all backup solutions approach deduplication in the same manner. Get to know your infrastructure and individual backup requirements. HPE can help you take the guesswork out of data optimization with a hybrid solution that balances the advantages of both backup- and target-focused data deduplication across your entire IT environment. Find out more about HPE InfoSight and how it can help your organization gain a cloud operational experience in managing apps and data from edge to cloud with the industry’s most advanced AI for infrastructure, ensuring that your environment is always on, always fast, and always agile. https://lnkd.in/dcMydb8M

Data lifecycle management addresses how to gain control of and capitalize upon the vast amounts of data most organizations possess. Enterprises that can break down their organizational silos and unify their data are more competitive and more successful than their peers. Accomplishing those goals requires careful organization of the five different phases that comprise the data lifecycle: creation, storage, usage, archiving, and destruction. To successfully manage data throughout its lifecycle, enterprises should listen to day-to-day users. Regulatory bodies and legal authorities also need to be taken into consideration to produce a successful model. #r2certified #ewaste #ewasterecycling #erecycling #ITAD #circulareconomy #datadesctruction

List of Top Data Governance Software 2023 - TrustRadius: List of Top Data Governance Software 2023  TrustRadius #datagovernance #CDO #finperform

Discover why Change Data Capture (CDC) is the go-to for modern #DataEngineering. It offers real-time precision, minimal resource use, and maintains transactional integrity, outshining traditional Batch Loading methods. 🚀

Data Lifecycle Management: Maximizing Efficiency for Your Business

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗮 𝗾𝘂𝗶𝗰𝗸 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗮𝘀𝘀𝗼𝗰𝗶𝗮𝘁𝗲𝗱 𝗿𝗶𝘀𝗸𝘀 𝗶𝗻 𝗮𝗹𝗹 𝘀𝘁𝗮𝗴𝗲𝘀! 💻 🛡️ The goal of 𝗗𝗮𝘁𝗮 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗗𝗟𝗠) is to ensure data is stored securely and efficiently, accessible when needed, and disposed again in a secure manner when no longer needed, throughout its entire lifecycle, from its creation to its eventual deletion. 💥 𝗚𝗼𝗮𝗹𝘀 𝗼𝗳 𝗗𝗟𝗠: 💠 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆: Ensure that data is accurate, complete, and consistent. 💠 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Data Lifecycle Management focuses on keeping your data secure. 💠 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Ensure the right data is available at the right time to the right users. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 : 💠 Assuring security. 💠 Maintaining efficiency. 💠 Providing your data with more value. 💠 Maintaining compliance with data retention regulations and requirements. 𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘃𝗮𝗿𝗶𝗼𝘂𝘀 𝗿𝗶𝘀𝗸𝘀 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝗱 𝗶𝗻 𝗮𝗹𝗹 𝘁𝗵𝗲 𝘀𝘁𝗲𝗽𝘀 𝗼𝗳 𝗗𝗟𝗠:- 💠 Step 1: Acquistion, Associated Risk- Low 💠 Step 2: Storage, Associated Risk- Low 💠 Step 3: Processing, Associated Risk- Moderate 💠 Step 4: Usage, Associated Risk- High 💠 Step 5: Disposal, Associated Risk- Moderate The following infographic (Credit: Arudaya Praveen) shows the details of various stages of Data lifecycle management. If you have any other points, put it in the comments! Source Credit: Sourabh Chakraborty 🟢 #datastorage #datadestruction #dataarchiving #datautilization #datasharing