Best Big Data Software in New Zealand - Page 8

Scuba enables self-service analytics on a large scale, catering to various roles such as product managers, business unit leaders, chief experience officers, data scientists, business analysts, and IT personnel who will find it incredibly easy to access their data and extract valuable insights. By using Interana, you can delve deeper into understanding customer behavior, system performance, and application usage—essentially anything related to actions over time—transcending traditional dashboards and static reports. This unique analytics platform empowers you and your team to dynamically explore your data in real-time, providing clarity not only on what is happening in your business but also offering explanations for those occurrences. With Scuba, there's no delay in accessing your data; everything is readily available, allowing you to pose questions as fast as they come to mind. Designed with everyday business users in mind, Scuba eliminates the need for coding skills or SQL knowledge, making data exploration accessible to all. Consequently, businesses can make timely, informed decisions based on real-time insights rather than outdated information.

One platform with a diverse range of solutions, INDICA seamlessly integrates with all company applications and data sources. It effectively indexes real-time data, providing a comprehensive view of your entire data environment. Built on this robust platform, INDICA presents four distinct solutions. The INDICA Enterprise Search feature grants access to all corporate data sources via a single interface, indexing both structured and unstructured data while prioritizing results based on relevance. Meanwhile, INDICA eDiscovery can be tailored for individual cases or structured to facilitate swift fraud or compliance investigations. The INDICA Privacy Suite equips organizations with a comprehensive set of tools to ensure adherence to GDPR and CCPA regulations, maintaining ongoing compliance. Additionally, INDICA Data Lifecycle Management empowers you to oversee your corporate data, enabling efficient tracking, cleaning, or migration. Overall, INDICA’s data platform is designed with a wide array of features, ensuring you can effectively manage and control your data landscape while adapting to evolving business needs. This flexibility allows organizations to respond proactively to data challenges and opportunities.

Strategizing, innovating, and advancing. From identification of needs to implementation of solutions. Introducing the eDrain DATA CLOUD PLATFORM. This platform is designed specifically for the collection, monitoring, and generation of comprehensive reports on data. It functions within the realm of Big Data, utilizing a driver-centric approach that facilitates the integration of various types of data. The advanced driver engine allows for the simultaneous incorporation of numerous data streams and devices. Its features include the ability to customize dashboards, add different views, and create tailored widgets, along with configuring new devices, flows, and sensors. Users can also set up custom reports, monitor sensor statuses, and manage real-time data flows. Additionally, it enables the definition of flow logic, analysis rules, and warning thresholds, as well as configuration for events and actions. New devices can be created and new stations configured, allowing for the effective management and verification of alerts, ensuring a comprehensive data management experience. This platform empowers users to take full control of their data environment.

Boost the pace of AI innovation through cloud-native data integration offered by IBM Cloud Pak for Data. With AI-driven data integration capabilities accessible from anywhere, the effectiveness of your AI and analytics is directly linked to the quality of the data supporting them. Utilizing a modern container-based architecture, IBM® DataStage® for IBM Cloud Pak® for Data ensures the delivery of superior data. This solution merges top-tier data integration with DataOps, governance, and analytics within a unified data and AI platform. By automating administrative tasks, it helps in lowering total cost of ownership (TCO). The platform's AI-based design accelerators, along with ready-to-use integrations with DataOps and data science services, significantly hasten AI advancements. Furthermore, its parallelism and multicloud integration capabilities enable the delivery of reliable data on a large scale across diverse hybrid or multicloud settings. Additionally, you can efficiently manage the entire data and analytics lifecycle on the IBM Cloud Pak for Data platform, which encompasses a variety of services such as data science, event messaging, data virtualization, and data warehousing, all bolstered by a parallel engine and automated load balancing features. This comprehensive approach ensures that your organization stays ahead in the rapidly evolving landscape of data and AI.

Delta Lake serves as an open-source storage layer that integrates ACID transactions into Apache Spark™ and big data operations. In typical data lakes, multiple pipelines operate simultaneously to read and write data, which often forces data engineers to engage in a complex and time-consuming effort to maintain data integrity because transactional capabilities are absent. By incorporating ACID transactions, Delta Lake enhances data lakes and ensures a high level of consistency with its serializability feature, the most robust isolation level available. For further insights, refer to Diving into Delta Lake: Unpacking the Transaction Log. In the realm of big data, even metadata can reach substantial sizes, and Delta Lake manages metadata with the same significance as the actual data, utilizing Spark's distributed processing strengths for efficient handling. Consequently, Delta Lake is capable of managing massive tables that can scale to petabytes, containing billions of partitions and files without difficulty. Additionally, Delta Lake offers data snapshots, which allow developers to retrieve and revert to previous data versions, facilitating audits, rollbacks, or the replication of experiments while ensuring data reliability and consistency across the board.

Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.

Apache Storm is a distributed computation system that is both free and open source, designed for real-time data processing. It simplifies the reliable handling of endless data streams, similar to how Hadoop revolutionized batch processing. The platform is user-friendly, compatible with various programming languages, and offers an enjoyable experience for developers. With numerous applications including real-time analytics, online machine learning, continuous computation, distributed RPC, and ETL, Apache Storm proves its versatility. It's remarkably fast, with benchmarks showing it can process over a million tuples per second on a single node. Additionally, it is scalable and fault-tolerant, ensuring that data processing is both reliable and efficient. Setting up and managing Apache Storm is straightforward, and it seamlessly integrates with existing queueing and database technologies. Users can design Apache Storm topologies to consume and process data streams in complex manners, allowing for flexible repartitioning between different stages of computation. For further insights, be sure to explore the detailed tutorial available.

We are excited to introduce a groundbreaking big data platform designed for the music industry, which consolidates all relevant information into a single, reliable source to inform strategic decisions. Within the music business sector, numerous data sources exist, but they are often isolated and disjointed. Our innovative platform effectively identifies and integrates these sources, establishing a robust foundation of high-quality data applicable to everyday operations in the music industry. To operate effectively and securely while uncovering unique insights, record labels and agencies need an advanced data management and governance framework that ensures data is consistently accessible, pertinent, and practical. As data sources are integrated into Wavo’s Big Data Platform, machine learning techniques are utilized to categorize the data according to customized templates, facilitating easy access and deep dives into crucial information. This capability empowers every member of a music organization to harness and utilize data that is prepared and organized for immediate application and value creation. Ultimately, our platform serves as a catalyst for smarter decision-making and enhanced operational efficiency across the music business landscape.

SmartHub Analytics is a specialized platform for telecom big-data analytics that focuses on financial and subscriber-centric ROI-driven applications. It is specifically developed to foster data sharing and reuse, thereby enhancing business performance and providing analytics that are instantly actionable. By breaking down silos, SmartHub Analytics can evaluate, verify, and model extensive datasets from TEOCO’s array of solutions, which encompass areas like customer management, planning, optimization, service assurance, geo-location, service quality, and costs. Additionally, as an extra analytics layer integrated with existing OSS and BSS systems, SmartHub Analytics establishes an independent analytics environment that has demonstrated substantial returns on investment, allowing operators to save billions. Our approach frequently reveals substantial cost reductions for clients through the application of predictive machine learning techniques. Moreover, SmartHub Analytics consistently leads the industry by offering rapid data analysis capabilities, ensuring that businesses can adapt and respond to market changes with agility and precision.

bi(OS)® offers an unmatched speed to insight for developers of data applications in a cohesive manner. With bi(OS)®, the entire process of creating data applications can be completed in just a matter of hours to days. This comprehensive approach encompasses the integration of diverse data sources, the extraction of real-time insights, and the smooth deployment into production environments. By joining forces with enterprise data teams across various sectors, you can transform into the data superhero your organization needs. The combination of Open Source, Cloud, and SaaS has not fulfilled its potential for delivering genuine data-driven results. Enterprises have largely focused their investments on data movement and integration, a strategy that is ultimately unsustainable. A fresh perspective on data management is urgently required, one that considers the unique challenges of enterprises. bi(OS)® is designed by rethinking fundamental principles in enterprise data management, ranging from data ingestion to insight generation. It caters to the needs of API, AI, and BI developers in a cohesive manner, enabling data-driven outcomes within days. As engineers collaborate effectively, a harmonious relationship emerges among IT teams, tools, and processes, creating a lasting competitive advantage for the organization.

EMR allows you to adjust the size of your managed Hadoop clusters either manually or automatically, adapting to your business needs and monitoring indicators. Its architecture separates storage from computation, which gives you the flexibility to shut down a cluster to optimize resource utilization effectively. Additionally, EMR features hot failover capabilities for CBS-based nodes, utilizing a primary/secondary disaster recovery system that enables the secondary node to activate within seconds following a primary node failure, thereby ensuring continuous availability of big data services. The metadata management for components like Hive is also designed to support remote disaster recovery options. With computation-storage separation, EMR guarantees high data persistence for COS data storage, which is crucial for maintaining data integrity. Furthermore, EMR includes a robust monitoring system that quickly alerts you to cluster anomalies, promoting stable operations. Virtual Private Clouds (VPCs) offer an effective means of network isolation, enhancing your ability to plan network policies for managed Hadoop clusters. This comprehensive approach not only facilitates efficient resource management but also establishes a reliable framework for disaster recovery and data security.

Bodo's robust computing engine and its parallel processing methodology ensure efficient performance and scalability, even when handling over 10,000 cores and petabytes of information. By leveraging standard Python APIs such as Pandas, Bodo accelerates the development process and simplifies the maintenance of data science, engineering, and machine learning tasks. It minimizes the risk of frequent failures through native code execution on bare-metal systems, allowing developers to detect issues prior to deployment with comprehensive end-to-end compilation. This enables quicker experimentation with vast datasets directly from your laptop, all while enjoying the inherent simplicity of Python. Additionally, you can produce code that is ready for production without the complications of extensive refactoring necessary for scaling on large infrastructures!

Apache Arrow establishes a columnar memory format that is independent of any programming language, designed to handle both flat and hierarchical data, which allows for optimized analytical processes on contemporary hardware such as CPUs and GPUs. This memory format enables zero-copy reads, facilitating rapid data access without incurring serialization delays. Libraries associated with Arrow not only adhere to this format but also serve as foundational tools for diverse applications, particularly in high-performance analytics. Numerous well-known projects leverage Arrow to efficiently manage columnar data or utilize it as a foundation for analytic frameworks. Developed by the community for the community, Apache Arrow emphasizes open communication and collaborative decision-making. With contributors from various organizations and backgrounds, we encourage inclusive participation in our ongoing efforts and developments. Through collective contributions, we aim to enhance the functionality and accessibility of data analytics tools.

Hypertable provides a high-performance, scalable database solution that enhances the efficiency of your big data applications while minimizing hardware usage. This platform offers exceptional efficiency and outperforms its competitors, leading to significant cost reductions for users. Its robust and proven architecture supports numerous services at Google. Users can enjoy the advantages of open-source technology backed by a vibrant and active community. With a C++ implementation, Hypertable ensures optimal performance. Additionally, it offers around-the-clock support for critical big data operations. Clients benefit from direct access to the expertise of the core developers behind Hypertable. Specifically engineered to address scalability challenges that traditional relational database management systems struggle with, Hypertable leverages a design model pioneered by Google to effectively tackle scaling issues, making it superior to other NoSQL alternatives available today. Its innovative approach not only resolves current scalability needs but also anticipates future demands in data management.

Utilize widely-used open-source frameworks like Apache Hadoop, Spark, Hive, and Kafka with Azure HDInsight, a customizable and enterprise-level service designed for open-source analytics. Effortlessly manage vast data sets while leveraging the extensive open-source project ecosystem alongside Azure’s global capabilities. Transitioning your big data workloads to the cloud is straightforward and efficient. You can swiftly deploy open-source projects and clusters without the hassle of hardware installation or infrastructure management. The big data clusters are designed to minimize expenses through features like autoscaling and pricing tiers that let you pay solely for your actual usage. With industry-leading security and compliance validated by over 30 certifications, your data is well protected. Additionally, Azure HDInsight ensures you remain current with the optimized components tailored for technologies such as Hadoop and Spark, providing an efficient and reliable solution for your analytics needs. This service not only streamlines processes but also enhances collaboration across teams.

Break down data silos through a unified storage solution that effectively optimizes expenses by employing tiered storage and comprehensive policy management. Enhance data authentication with Azure Active Directory (Azure AD) alongside role-based access control (RBAC), while bolstering data protection with features such as encryption at rest and advanced threat protection. This approach ensures a highly secure environment with adaptable mechanisms for safeguarding access, encryption, and network-level governance. Utilizing a singular storage platform, you can seamlessly ingest, process, and visualize data while supporting prevalent analytics frameworks. Cost efficiency is further achieved through the independent scaling of storage and compute resources, lifecycle policy management, and object-level tiering. With Azure's extensive global infrastructure, you can effortlessly meet diverse capacity demands and manage data efficiently. Additionally, conduct large-scale analytical queries with consistently high performance, ensuring that your data management meets both current and future needs.

Harness the power of your data and create innovative artificial intelligence (AI) solutions using Azure Databricks, where you can establish your Apache Spark™ environment in just minutes, enable autoscaling, and engage in collaborative projects within a dynamic workspace. This platform accommodates multiple programming languages such as Python, Scala, R, Java, and SQL, along with popular data science frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. With Azure Databricks, you can access the most current versions of Apache Spark and effortlessly connect with various open-source libraries. You can quickly launch clusters and develop applications in a fully managed Apache Spark setting, benefiting from Azure's expansive scale and availability. The clusters are automatically established, optimized, and adjusted to guarantee reliability and performance, eliminating the need for constant oversight. Additionally, leveraging autoscaling and auto-termination features can significantly enhance your total cost of ownership (TCO), making it an efficient choice for data analysis and AI development. This powerful combination of tools and resources empowers teams to innovate and accelerate their projects like never before.

Varada offers a cutting-edge big data indexing solution that adeptly balances performance and cost while eliminating the need for data operations. This distinct technology acts as an intelligent acceleration layer within your data lake, which remains the central source of truth and operates within the customer's cloud infrastructure (VPC). By empowering data teams to operationalize their entire data lake, Varada facilitates data democratization while ensuring fast, interactive performance, all without requiring data relocation, modeling, or manual optimization. The key advantage lies in Varada's capability to automatically and dynamically index pertinent data, maintaining the structure and granularity of the original source. Additionally, Varada ensures that any query can keep pace with the constantly changing performance and concurrency demands of users and analytics APIs, while also maintaining predictable cost management. The platform intelligently determines which queries to accelerate and which datasets to index, while also flexibly adjusting the cluster to match demand, thereby optimizing both performance and expenses. This holistic approach to data management not only enhances operational efficiency but also allows organizations to remain agile in an ever-evolving data landscape.

Doolytic is at the forefront of big data discovery, integrating data exploration, advanced analytics, and the vast potential of big data. The company is empowering skilled BI users to participate in a transformative movement toward self-service big data exploration, uncovering the inherent data scientist within everyone. As an enterprise software solution, doolytic offers native discovery capabilities specifically designed for big data environments. Built on cutting-edge, scalable, open-source technologies, doolytic ensures lightning-fast performance, managing billions of records and petabytes of information seamlessly. It handles structured, unstructured, and real-time data from diverse sources, providing sophisticated query capabilities tailored for expert users while integrating with R for advanced analytics and predictive modeling. Users can effortlessly search, analyze, and visualize data from any format and source in real-time, thanks to the flexible architecture of Elastic. By harnessing the capabilities of Hadoop data lakes, doolytic eliminates latency and concurrency challenges, addressing common BI issues and facilitating big data discovery without cumbersome or inefficient alternatives. With doolytic, organizations can truly unlock the full potential of their data assets.

Effortlessly leverage your entire system's data with our SHREWD Platform, which features advanced tools and open APIs. The SHREWD Platform is equipped with integration and data collection tools that support the operations of various SHREWD modules. These tools consolidate data and securely store it in a UK-based data lake. Subsequently, the data can be accessed by SHREWD modules or through an API, allowing for the transformation of raw information into actionable insights tailored to specific needs. The platform can ingest data in virtually any format, whether it’s in traditional spreadsheets or through modern digital systems via APIs. Additionally, the system’s open API facilitates third-party connections, enabling external applications to utilize the information stored in the data lake when necessary. By providing an operational data layer that serves as a real-time single source of truth, the SHREWD Platform empowers its modules to deliver insightful analytics, enabling managers and decision-makers to act promptly and effectively. This holistic approach to data management ensures that organizations can remain agile and responsive to changing demands.

IBM Sterling Fulfillment Optimizer powered by Watson is an advanced cognitive analytics platform that significantly improves the functionality of current order management systems. This innovative solution serves as a "big data brain," providing enhanced order management and inventory visibility for retailers involved in eCommerce fulfillment. By leveraging Fulfillment Optimizer, retailers gain deeper insights and can respond promptly to market fluctuations, allowing them to strike an ideal balance between maintaining profit margins, optimizing store capacity, and fulfilling delivery commitments. The informed sourcing decisions made possible by this tool can lead to substantial profit increases, particularly during high-demand periods. Additionally, it enables retailers to assess the ramifications of omnichannel strategies across various sectors including eCommerce, merchandising, logistics, store operations, and supply chain management. By smartly balancing the costs associated with omnichannel fulfillment against service quality, retailers can safeguard their profit margins while maximizing the utilization of store capacity and ensuring timely deliveries to customers. Furthermore, the platform simplifies the execution of optimized omnichannel fulfillment strategies, ensuring the lowest possible cost of service while meeting customer expectations effectively.

IBM® Sterling Transformation Extender empowers organizations to seamlessly integrate transactions involving customers, suppliers, and business partners across their entire operations. This tool automates the intricate processes of data transformation and validation, accommodating a wide array of formats and standards. Users can execute data transformations in both on-premises settings and cloud environments. Furthermore, it offers advanced transformation capabilities that include metadata for mapping, compliance verification, and related processing functionalities tailored to specific sectors, such as finance, healthcare, and supply chain management. The system supports both structured and unstructured data, along with custom formats, and is compatible with on-premises, hybrid, private, and public cloud configurations. With a strong focus on user experience, it features RESTful APIs for enhanced functionality. The solution facilitates complex transformations and validation of data across multiple formats, enabling any-to-any data transformation while being containerized for cloud deployment. Additionally, it includes industry-specific packs to further streamline operations and enhance efficiency.

Leverage cutting-edge, actionable analytics to enhance your manufacturing effectiveness, speed up the introduction of new products, and simultaneously improve their reliability. By utilizing the foremost big data analytics platform and years of specialized knowledge, you can elevate the efficiency, quality, and dependability of your manufacturing processes. Furthermore, gain crucial insights into your supply chain while maximizing manufacturing performance and accelerating the product development cycle. As a lifecycle analytics firm, we empower automotive and semiconductor manufacturers to fully utilize their data. Our innovative open platform is meticulously crafted for your sector, offering an in-depth understanding of all product attributes and fostering innovation through a holistic end-to-end solution that incorporates advanced analytics, artificial intelligence, and machine learning, setting the foundation for future advancements. This comprehensive approach ensures that you not only stay competitive but also lead in your industry.

As interactions with customers increasingly transition from physical to digital environments, it becomes necessary to move beyond traditional face-to-face conversations. Instead, customers now convey their preferences and requirements through data. Gaining insights into customer behavior and validating our preconceptions about them also relies heavily on data-driven approaches. However, stringent privacy laws like GDPR and CCPA complicate this deep understanding even further. The MOSTLY AI synthetic data platform effectively addresses this widening gap in customer insights. This reliable and high-quality synthetic data generator supports businesses across a range of applications. Offering privacy-compliant data alternatives is merely the starting point of its capabilities. In terms of adaptability, MOSTLY AI's synthetic data platform outperforms any other synthetic data solution available. The platform's remarkable versatility and extensive use case applicability establish it as an essential AI tool and a transformative resource for software development and testing. Whether for AI training, enhancing explainability, mitigating bias, ensuring governance, or generating realistic test data with subsetting and referential integrity, MOSTLY AI serves a broad spectrum of needs. Ultimately, its comprehensive features empower organizations to navigate the complexities of customer data while maintaining compliance and protecting user privacy.

Currently, less than 10% of the vast $260 billion big data industry is being utilized, primarily due to outdated processes and the overpowering presence of intermediaries. Our goal is to democratize this market, enabling access to the remaining 90% of data that is currently untapped for sharing. We aim to establish a decentralized framework that creates a data oracle network, utilizing an open protocol that facilitates interaction among participants while fostering a sustainable economy. Our multifunctional decentralized application (DAPP) and crypto wallet provide users with the opportunity to earn rewards for the data they generate, alongside access to various decentralized finance (DeFi) tools through a seamless user experience. The GeoDB marketplace empowers data buyers globally to acquire data produced by users through applications linked to the GeoDB platform. Participants, known as data sources, contribute data that is uploaded via our proprietary and partner applications, while validators ensure the efficient transfer and verification of contracts through blockchain technology, allowing for a streamlined and decentralized process. This innovative approach not only enhances data accessibility but also promotes a collaborative environment for all stakeholders involved.