Latest news with #dataLake
Zawya
22-05-2025
- Business
- Zawya
Huawei releases AI Data Lake solution, positioned to accelerate industry intelligence
Tashkent, Uzbekistan: Today at the 4th Huawei Innovative Data Storage Summit, Huawei introduced its new AI Data Lake Solution, designed to help industries implement artificial intelligence more effectively. The announcement came during a keynote address titled "Data Awakening, Accelerating Intelligence with AI-Ready Data Infrastructure," delivered by Yuan Yuan, Vice President of Huawei Data Storage Product Line. While digital transformation has evolved over decades and brought sweeping change, one thing remains constant: the critical importance of data. This was highlighted in Yuan's speech: "To be Al-ready, get data-ready. The continuous deepening of industry digitalization is a process of transforming data into information and knowledge." The AI Data Lake Solution integrates four main components—data storage, data management, resource management, and AI toolchain—to help deliver AI corpus data and improve model training and inference capabilities. In his address, Yuan provided details about the products and technologies that are part of the Data Lake solution: Data storage: continuous innovation in performance, capacity, and resilience Accelerated AI model training and inference: The Huawei OceanStor A series high-performance AI storage delivers exceptional performance. For instance, it enabled the AI technologies developer iFLYTEK, among others, to significantly boost cluster training efficiency. Its advanced inference acceleration technology enhances inference performance, reduces latency, and elevates the application user experience, accelerating the deployment of large-model inference applications in production environments. Efficient storage of mass AI data: The OceanStor Pacific All-Flash Scale-Out Storage offers a high-capacity density of 4 PB/2 U and ultra-low power consumption of 0.25 W/TB. Designed to manage exabyte-scale data with ease, it is well-suited for data-intensive workloads across education, scientific research, medical imaging, and media. AI corpus and vector database backup: Huawei's OceanProtect Backup Storage provides 10 times higher backup performance than other mainstream options and boasts 99.99% ransomware attack detection accuracy, safeguarding key data of training corpus and vector databases in fields like oil and gas and MSPs. Data management: data visibility, manageability, and mobility across regions Huawei's Data Center Management (DME) platform incorporates the Omni-Dataverse to help reduce data silos across geographically distributed data centers. Its data retrieval system can process over 100 billion files in seconds, helping organizations access and utilize their data more efficiently. Resource management: pooling of diverse xPUs and intelligent scheduling of AI resources The Datacenter Virtualization Solution (DCS) platform uses virtualization and container technologies to provide xPU resource pooling and scheduling, helping to improve resource utilization. The DataMaster in DME offers AI-powered operations and maintenance with AI Copilot, including features such as Q&A, O&M assistance, and inspection tools to support IT operations. With the full arrival of the intelligent era, data has become the core resource driving the development of artificial intelligence. Shahin Hashim, Associate Research Director at IDC pointed out, 'In the AI era, building ultra scalable, efficient and sustainable data infrastructure is crucial, and the key lies in achieving performance at scale, frictionless data mobility, strong governance, and resilience by design.' Addressing enterprises' critical data storage demands in the AI era, Jun Liu, Vice President of All-Flash Storage Domain, Huawei Data Storage Product Line delivered a keynote speech and unveiled the next-generation OceanStor Dorado converged all-flash storage. This innovative solution is designed to accelerate enterprise digital and intelligent transformation through converged, resilient, and intelligent all-flash storage capabilities. With emerging technology trends like AI, new security threats such as ransomware attacks have also surfaced. Yahya Kassab, General Manager - KSA, Gulf & Pakistan, Commvault, stated: "Business-critical IT data infrastructure must demonstrate greater resilience against potential security threats. Commvault and Huawei OceanProtect have built a joint solution that comprehensively safeguards data security and reliability through backup, ransomware detection, encryption, and other technologies." -Ends- About Huawei Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We have more than 208,000 employees, and we operate in more than 170 countries and regions, serving more than three billion people around the Vision and mission is to bring digital to every person, home and organization for a fully connected, intelligent world. To this end, we will drive ubiquitous connectivity and promote equal access to networks; bring cloud and artificial intelligence to all four corners of the earth to provide superior computing power where you need it, when you need it; build digital platforms to help all industries and organizations become more agile, efficient, and dynamic; redefine user experience with AI, making it more personalized for people in all aspects of their life, whether they're at home, in the office, or on the go. For more information, please visit Huawei online at or follow us on: Middle East:
Geeky Gadgets
07-05-2025
- Business
- Geeky Gadgets
Why Your Data Lake Strategy is Failing and How Apache Iceberg Can Save It
What if the very foundation of your organization's data strategy was holding you back? For years, data lakes have promised flexibility and scalability, yet many businesses find themselves grappling with fragmented systems, unreliable analytics, and outdated schemas. Enter Apache Iceberg—a innovative open table format that's quietly transforming the way we think about data lake management. Originally developed by Netflix to tackle the inefficiencies of traditional architectures, Iceberg introduces relational database-like capabilities to data lakes, offering a structured, scalable, and consistent framework for modern data needs. It's no wonder the tech world is abuzz with its potential. In this overview, the team at Confluent Developer explore why Apache Iceberg is becoming a cornerstone of modern data architectures. You'll uncover how it solves persistent challenges like schema evolution, data consistency, and transactional limitations—issues that have long plagued traditional data lakes. From its snapshot-based consistency to seamless integration with tools like Kafka and Spark, Iceberg is more than just a technical upgrade; it's a paradigm shift. Whether you're managing streaming data, real-time analytics, or massive datasets, Iceberg's innovative approach could redefine how your organization handles data. So, what makes this technology so indispensable in today's data-driven world? Let's unravel the story. Apache Iceberg Overview Challenges in Traditional Data Lakes The transition from data warehouses to data lakes introduced greater flexibility in handling raw, unstructured data. However, this shift also brought significant challenges that limited the effectiveness of traditional data lakes. These challenges include: Schema Management: Traditional data lakes often struggle with schema evolution, making it difficult to update schemas without breaking existing queries or workflows. Traditional data lakes often struggle with schema evolution, making it difficult to update schemas without breaking existing queries or workflows. Data Consistency: Making sure consistent data operations across distributed environments has been a persistent issue, leading to unreliable analytics and processing. Making sure consistent data operations across distributed environments has been a persistent issue, leading to unreliable analytics and processing. Transactional Limitations: Many data lakes lack robust support for updates, deletes, or upserts, which are critical for maintaining data accuracy and integrity. These limitations have made it challenging for organizations to maintain data integrity, perform advanced analytics, and support real-time processing. As a result, many data lake implementations have become fragmented and inefficient, requiring innovative solutions to address these shortcomings. Core Features of Apache Iceberg Apache Iceberg was designed to overcome the limitations of traditional data lakes by introducing a range of advanced features that enhance scalability, consistency, and usability. Key features include: Open Table Format: Iceberg provides a standardized framework for managing data in distributed file systems, making sure compatibility across tools and scalability for growing datasets. Iceberg provides a standardized framework for managing data in distributed file systems, making sure compatibility across tools and scalability for growing datasets. Schema Evolution: Iceberg allows seamless schema updates without disrupting existing queries, allowing organizations to adapt to changing data requirements effortlessly. Iceberg allows seamless schema updates without disrupting existing queries, allowing organizations to adapt to changing data requirements effortlessly. Snapshot-Based Consistency: By using snapshots, Iceberg ensures reliable and consistent data operations, even in complex distributed environments. By using snapshots, Iceberg ensures reliable and consistent data operations, even in complex distributed environments. Logical Data Organization: Data is structured into columnar formats like Parquet, with changes tracked using JSON metadata. This approach integrates with catalog systems for efficient table management. These features make Apache Iceberg a robust and versatile solution for managing large and evolving datasets, empowering organizations to unlock the full potential of their data lakes. Apache Iceberg Explained Watch this video on YouTube. Below are more guides on artificial intelligence (AI) from our extensive range of articles. How Apache Iceberg Operates The architecture of Apache Iceberg is built on three interconnected layers, each serving a critical role in its functionality: Data Layer: This layer stores raw data in columnar formats such as Parquet, optimizing both storage efficiency and query performance. This layer stores raw data in columnar formats such as Parquet, optimizing both storage efficiency and query performance. Metadata Layer: Iceberg tracks data and schema changes over time using manifest files, manifest lists, and metadata files. This ensures consistency, traceability, and efficient data management. Iceberg tracks data and schema changes over time using manifest files, manifest lists, and metadata files. This ensures consistency, traceability, and efficient data management. Catalog Layer: The catalog layer maps table names to metadata files using systems like Hive Metastore or JDBC databases, simplifying data discovery and allowing seamless querying. This layered design ensures that Iceberg remains lightweight, flexible, and capable of handling the demands of large-scale datasets while maintaining high performance and reliability. Flexibility and Ecosystem Integration One of Apache Iceberg's most notable strengths is its flexibility and ability to integrate seamlessly with a wide range of tools and platforms. Unlike traditional systems, Iceberg is a specification rather than a server, making it highly adaptable. Key integration features include: Multi-Language Support: Iceberg is compatible with popular programming languages such as Java, Python, Flink, and Spark, allowing developers across ecosystems to use its capabilities. Iceberg is compatible with popular programming languages such as Java, Python, Flink, and Spark, allowing developers across ecosystems to use its capabilities. Advanced Querying: Iceberg integrates with tools like Presto and Trino, allowing users to perform complex analytics, joins, and aggregations with ease. Iceberg integrates with tools like Presto and Trino, allowing users to perform complex analytics, joins, and aggregations with ease. Catalog Integration: By integrating with Hive Metastore and JDBC databases, Iceberg ensures compatibility with existing infrastructure, reducing the need for extensive reconfiguration. This adaptability makes Iceberg a versatile choice for modern data architectures, allowing organizations to build scalable and future-proof systems. Relational Capabilities for Data Lakes Apache Iceberg bridges the gap between traditional data lakes and relational databases by introducing relational semantics to data lake environments. With support for operations such as updates, deletes, and upserts, Iceberg enables precise and reliable data management. These capabilities, traditionally associated with transactional databases, empower organizations to maintain data accuracy and integrity at scale. Additionally, Iceberg supports real-time data processing, making it well-suited for streaming use cases where data freshness is critical. By allowing real-time updates and schema changes, Iceberg ensures that data remains consistent and up-to-date, even in dynamic and fast-paced environments. Applications in Streaming Data Apache Iceberg is particularly effective in streaming data scenarios, where real-time processing and consistency are essential. For example, Iceberg integrates seamlessly with tools like Kafka to enable real-time updates and schema evolution. Confluent's 'table flow' feature, for instance, maps Kafka topics directly to Iceberg tables, eliminating the need for batch processing. This integration ensures that data remains consistent and accessible for analytics, even as it evolves in real time. By supporting streaming data workflows, Iceberg enables organizations to build systems that can handle dynamic environments and deliver actionable insights without delays. The Role of Apache Iceberg in Modern Data Architectures Apache Iceberg is emerging as a cornerstone of modern data architectures. Its support for advanced features such as transactional operations, schema evolution, and real-time processing positions it as a versatile solution for a wide range of use cases. Whether your focus is on batch processing, real-time analytics, or streaming data, Iceberg provides the tools and flexibility needed to meet the demands of today's data-driven world. As organizations continue to adopt streaming systems and real-time analytics, the importance of Iceberg's capabilities will only grow. By addressing critical challenges in data lake management, Iceberg enables you to build scalable, reliable, and efficient data architectures that are ready for the future. Media Credit: Confluent Developer Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
