Best Data Management Software in the Middle East - Page 66

HyperGraphDB serves as a versatile, open-source data storage solution founded on the sophisticated knowledge management framework of directed hypergraphs. Primarily created for persistent memory applications in knowledge management, artificial intelligence, and semantic web initiatives, it can also function as an embedded object-oriented database suitable for Java applications of varying scales, in addition to serving as a graph database or a non-SQL relational database. Built upon a foundation of generalized hypergraphs, HyperGraphDB utilizes tuples as its fundamental storage units, which can consist of zero or more other tuples; these individual tuples are referred to as atoms. The data model can be perceived as relational, permitting higher-order, n-ary relationships, or as graph-based, where edges can connect to an arbitrary assortment of nodes and other edges. Each atom is associated with a strongly-typed value that can be customized extensively, as the type system that governs these values is inherently embedded within the hypergraph structure. This flexibility allows developers to tailor the database according to specific project requirements, making it a robust choice for a wide range of applications.

RecallGraph is a versioned graph data store. It retains all changes its data (vertices, edges) have undergone to get to their current state. It supports point-in time graph traversals that allow the user to query any past state of a graph as well as the present. RecallGraph can be used in situations where data is best represented using a network of edges and vertices (i.e., as a graph). 1. Both edges and vertices can contain properties in the form attribute/value pairs (equivalent of JSON objects). 2. Documents (vertices/edges), can change throughout their lives (both in their individual attributes/values as well as in their relationships to each other). 3. Documents from the past are just as important as their current states, so it is essential to retain and queryable their change history. Also see this blog post for an intro - https://blog.recallgraph.tech/never-lose-your-old-data-again.

Organizations that choose a do-it-yourself method for implementing a graph database should anticipate a timeline of about 2 to 3 months to achieve a production-ready state. In contrast, with Graph Story’s managed services, your operational database can be set up in just minutes. Discover various graph use cases and explore a side-by-side analysis of self-hosting versus managed services. We can accommodate deployments in your existing infrastructure, whether it's on AWS, Azure, or Google Compute Engine, in any geographical location. If you require VPC peering or IP access restrictions, we can easily adapt to your needs. For those looking to create a proof of concept, initiating a single enterprise graph instance only takes a few clicks. Should you need to scale up to a high-availability, production-ready cluster on demand, we are prepared to assist! Our graph database management tools are designed to simplify your experience, allowing you to monitor CPU, memory, and disk usage effortlessly. You also have access to configurations, logs, and the ability to backup your database and restore snapshots whenever necessary. This level of flexibility ensures that your graph database management aligns perfectly with your operational requirements.

HugeGraph is a high-performance and scalable graph database capable of managing billions of vertices and edges efficiently due to its robust OLTP capabilities. This database allows for seamless storage and querying, making it an excellent choice for complex data relationships. It adheres to the Apache TinkerPop 3 framework, enabling users to execute sophisticated graph queries using Gremlin, a versatile graph traversal language. Key features include Schema Metadata Management, which encompasses VertexLabel, EdgeLabel, PropertyKey, and IndexLabel, providing comprehensive control over graph structures. Additionally, it supports Multi-type Indexes that facilitate exact queries, range queries, and complex conditional queries. The platform also boasts a Plug-in Backend Store Driver Framework that currently supports various databases like RocksDB, Cassandra, ScyllaDB, HBase, and MySQL, while also allowing for easy integration of additional backend drivers as necessary. Moreover, HugeGraph integrates smoothly with Hadoop and Spark, enhancing its data processing capabilities. By drawing on the storage structure of Titan and the schema definitions from DataStax, HugeGraph offers a solid foundation for effective graph database management. This combination of features positions HugeGraph as a versatile and powerful solution for handling complex graph data scenarios.

Trustgrid is the SD-WAN for software providers. The Trustgrid platform uniquely addresses the needs of SaaS application providers who rely on customer or partner-controlled environments. By combining an SD-WAN 2.0, edge computing, and zero trust remote access into a single platform we allow software providers to manage and support distributed application environments from the cloud to the edge. Simplify connectivity, enhance security, and guarantee network availability with Trustgrid.

Microsoft and Intel have only recommended PCmover®, Enterprise. It will save you hours of IT time and resources during your PC hardware and OS updates - more than $300 per computer according to industry research Large organizations and enterprises can save up to 60% on both hard- and soft costs by using PCmover®. It is Microsoft's only recommended solution for migration. It can be used for any type of transfer scenario - break/fix, ongoing computer and OS refreshes for any network size. Remote Home Office Migrations – PCmover®, Enterprise wins direct competition with USMT and other tools, meeting the demands of central IT departments. Key Features - Automated PC migrations - Multiple PC Migration Scenarios - Simple Implementation - Enterprise Class Support - Zero-Touch Computer Migrations Remote PC Migrations

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

For your teams to make informed, data-driven choices, they require reliable and consistent information. Achieve this through the implementation of integrated, automated, and intelligent data governance on a large scale. Axon Data Governance serves as the central hub for collaboration and a data marketplace essential for effective and scalable governance initiatives. It allows for easy identification of stakeholders and promotes knowledge sharing across different communities, enabling teams to learn from one another. By establishing a well-curated data marketplace, teams can swiftly locate, access, and comprehend the data necessary to derive analytical insights. Leveraging governed data can enhance critical projects, such as elevating customer experiences, while ensuring that your organization produces reliable and consistent outcomes. Incorporating governance and data privacy considerations into your projects and processes from the outset is crucial for complying with regulations like GDPR and CCPA. Moreover, creating a unified data dictionary will ensure that business context remains consistent across various tools, fostering better collaboration and understanding among team members. This systematic approach not only streamlines data management but also enhances overall organizational efficiency.

Allow Ethyca to handle all your CCPA and GDPR responsibilities, freeing up your team’s valuable time with a comprehensive overview of your organization’s data supply chain. With Ethyca, managing Subject Access, Edit, and Erasure Requests becomes effortless, or if you prefer, it can be done entirely without your intervention. Streamline the process of returning categorized user data based on processing activities upon request, while enabling individuals to easily select and correct their data with Ethyca’s capability to access specific attributes. Simplify your CCPA compliance efforts by allowing Ethyca to manage all "do not sell my data" requests efficiently. Instead of racing around the office daily to adjust user consent preferences, you can simply rely on Ethyca. The platform connects cookie consent directly to the processing activities tied to each user's personal data record. Additionally, Ethyca features a user-friendly, customizable interface that empowers your users to manage their consent preferences effortlessly, ensuring a seamless experience. This way, you enhance user trust and compliance without overburdening your team.

Empower individuals to explore and analyze streaming data effectively. By sharing, documenting, and organizing your data, you can boost productivity by as much as 95%. Once you have your data, you can create applications tailored for real-world use cases. Implement a security model focused on data to address the vulnerabilities associated with open source technologies, ensuring data privacy is prioritized. Additionally, offer secure and low-code data pipeline functionalities that enhance usability. Illuminate all hidden aspects and provide unmatched visibility into data and applications. Integrate your data mesh and technological assets, ensuring you can confidently utilize open-source solutions in production environments. Lenses has been recognized as the premier product for real-time stream analytics, based on independent third-party evaluations. With insights gathered from our community and countless hours of engineering, we have developed features that allow you to concentrate on what generates value from your real-time data. Moreover, you can deploy and operate SQL-based real-time applications seamlessly over any Kafka Connect or Kubernetes infrastructure, including AWS EKS, making it easier than ever to harness the power of your data. By doing so, you will not only streamline operations but also unlock new opportunities for innovation.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) simplifies the process of creating and operating applications that leverage Apache Kafka for handling streaming data. As an open-source framework, Apache Kafka enables the construction of real-time data pipelines and applications. Utilizing Amazon MSK allows you to harness the native APIs of Apache Kafka for various tasks, such as populating data lakes, facilitating data exchange between databases, and fueling machine learning and analytical solutions. However, managing Apache Kafka clusters independently can be quite complex, requiring tasks like server provisioning, manual configuration, and handling server failures. Additionally, you must orchestrate updates and patches, design the cluster to ensure high availability, secure and durably store data, establish monitoring systems, and strategically plan for scaling to accommodate fluctuating workloads. By utilizing Amazon MSK, you can alleviate many of these burdens and focus more on developing your applications rather than managing the underlying infrastructure.

Apache Ranger™ serves as a framework designed to facilitate, oversee, and manage extensive data security within the Hadoop ecosystem. The goal of Ranger is to implement a thorough security solution throughout the Apache Hadoop landscape. With the introduction of Apache YARN, the Hadoop platform can effectively accommodate a genuine data lake architecture, allowing businesses to operate various workloads in a multi-tenant setting. As the need for data security in Hadoop evolves, it must adapt to cater to diverse use cases regarding data access, while also offering a centralized framework for the administration of security policies and the oversight of user access. This centralized security management allows for the execution of all security-related tasks via a unified user interface or through REST APIs. Additionally, Ranger provides fine-grained authorization, enabling specific actions or operations with any Hadoop component or tool managed through a central administration tool. It standardizes authorization methods across all Hadoop components and enhances support for various authorization strategies, including role-based access control, thereby ensuring a robust security framework. By doing so, it significantly strengthens the overall security posture of organizations leveraging Hadoop technologies.

CData Connect Real-time operational and business data is critical for your organization to provide actionable insights and drive growth. CData Connect is the missing piece in your data value chain. CData Connect allows direct connectivity to any application that supports standard database connectivity. This includes popular cloud BI/ETL applications such as: - Amazon Glue - Amazon QuickSight Domo - Google Apps Script - Google Cloud Data Flow - Google Cloud Data Studio - Looker - Microsoft Power Apps - Microsoft Power Query - MicroStrategy Cloud - Qlik Sense Cloud - SAP Analytics Cloud SAS Cloud SAS Viya - Tableau Online ... and many other things! CData Connect acts as a data gateway by translating SQL and securely proxying API calls.

BPRISE is a tech company focused on advertising and marketing, aimed at connecting brands and retailers with their mobile-centric customers both online and offline through strategically placed ads that respond to specific locations, while also assessing the effectiveness of these ads in driving conversions and engagement. Our offering: A Comprehensive Marketing Suite Designed to Connect with Your Digital-Focused Customers Across All Platforms. The marketing solution, known as ‘BPRISE Blend,’ enhances the effectiveness of advertising campaigns, provides in-depth marketing attribution analysis, and visualizes the customer journey along the purchasing process, allowing for precise measurement of the return on investment for digital marketing efforts. Advertisers can achieve highly personalized interactions in their digital campaigns. When should a Demand-Side Platform (DSP) be utilized to optimize the return on investment from advertising expenditures while interacting with each customer on a personal level? Brands can acquire genuine insights into their human consumers. What valuable customer data can be gathered from both online and offline channels to create lucrative customer segments? Additionally, BPRISE's innovative approach ensures that brands stay relevant in an ever-evolving digital landscape.

Business intelligence that is straightforward and user-friendly is now available to ALL businesses. There’s no need for coding, integration, or the creation of intricate dashboards; it’s all set and ready for use! With everything consolidated into a single dashboard, the pre-built home interface encapsulates your marketing reports and insights, utilizing a unified metric that aligns all platforms, allowing you to effortlessly identify which activities and channels are excelling. It is compatible with desktop, tablet, and mobile devices, simplifying your business metrics while providing easily digestible insights through the intuitive Turnkey Intelligence dashboard. This platform is crafted to ensure that both business users and marketing professionals are aligned. In mere seconds, you can determine what is thriving and what is lagging. Our user-friendly system can be operational within minutes, requiring no technical expertise or coding. Experience the convenience of viewing all your business data, insights, and marketing initiatives seamlessly on one dashboard, empowering you to make informed decisions quickly.

Achieve rapid automation for your data warehouse with Vaultspeed, an innovative tool adhering to the Data Vault 2.0 standards and backed by a decade of practical experience in data integration. This solution supports a comprehensive range of Data Vault 2.0 objects and offers various implementation options. It enables the swift generation of high-quality code across all scenarios within a Data Vault 2.0 integration framework. By integrating Vaultspeed into your existing setup, you can maximize your investments in both tools and expertise. You will also enjoy guaranteed compliance with the most recent Data Vault 2.0 standard, thanks to our ongoing collaboration with Scalefree, the authoritative knowledge source for the Data Vault 2.0 community. The Data Vault 2.0 modeling methodology simplifies model components to their essential elements, facilitating a uniform loading pattern and consistent database structure. Furthermore, Vaultspeed utilizes a template system that comprehensively understands the various object types and includes straightforward configuration settings, enhancing user experience and efficiency in data management.

Moon Modeler is a modern data modeling tool for MongoDB and Mongoose. Draw diagrams, visualize existing database structures and generate interactive documentation or schema creation or validation scripts. Supported platforms: - MongoDB - Mongoose Key features: - Easy and comfortable visual schema design - Visualization of existing MongoDB structures - Support for SSH/SSL/TLS connections - Support for modeling of nested objects - Generation of interactive HTML reports - Generation of schema validation scripts - Various themes and styles for reports - Support for sub-diagrams - Multiple display modes

Now, anyone can quickly uncover insights buried within their company data in just seconds. With search functionality, users can analyze their data and receive automated insights precisely when needed. ThoughtSpot enables anyone to pose questions, discover insights, and delve deeply into their organizational data almost instantaneously. No longer do you have to wait for specialized reports from data professionals; you can now address ad-hoc data inquiries immediately. This empowers individuals without technical backgrounds to find answers to their own data-related questions while you create a comprehensive source of truth that maintains security and governance across the board. By maximizing the potential of your cloud data warehouse, you can enhance the speed at which insights are obtained for everyone in your organization. In just minutes, democratize access to insights and revolutionize how your company leverages data. Explore how leading-edge companies are utilizing ThoughtSpot to extract greater value from their data resources. Furthermore, it can be deployed as either SaaS or as software within your private virtual cloud, ensuring that AI-driven insights are available to you more promptly than ever before. Ultimately, this transformative approach to data usage can lead to significant improvements in business decision-making.

The BiG EVAL platform offers robust software tools essential for ensuring and enhancing data quality throughout the entire information lifecycle. Built on a comprehensive and versatile code base, BiG EVAL's data quality management and testing tools are designed for peak performance and adaptability. Each feature has been developed through practical insights gained from collaborating with our clients. Maintaining high data quality across the full lifecycle is vital for effective data governance and is key to maximizing business value derived from your data. This is where the BiG EVAL DQM automation solution plays a critical role, assisting you with all aspects of data quality management. Continuous quality assessments validate your organization’s data, furnish quality metrics, and aid in addressing any quality challenges. Additionally, BiG EVAL DTA empowers you to automate testing processes within your data-centric projects, streamlining operations and enhancing efficiency. By integrating these tools, organizations can achieve a more reliable data environment that fosters informed decision-making.

Toad® Data Point is a versatile self-service data integration solution designed to streamline the processes of data access, preparation, and provisioning across multiple platforms. With its extensive data connectivity options, users can easily integrate data from a variety of sources, such as SQL and NoSQL databases, ODBC, as well as business intelligence tools and Microsoft Excel or Access. The application features a user-friendly Workbook interface that allows business users to build visual queries and automate workflows with ease. Regardless of your technical expertise, you can create queries without the need to write or modify SQL code, although those familiar with SQL will appreciate the intuitive graphical interface that enhances the creation of relationships and the visualization of queries. Toad Data Point Professional accommodates different user preferences by offering two distinct interfaces: one that emphasizes traditional flexibility and a wide range of functionalities. Additionally, this powerful tool ensures that data profiling tasks are efficiently managed, allowing users to achieve consistent and reliable results across their projects.

The data refinery tool, which can be accessed through IBM Watson® Studio and Watson™ Knowledge Catalog, significantly reduces the time spent on data preparation by swiftly converting extensive volumes of raw data into high-quality, usable information suitable for analytics. Users can interactively discover, clean, and transform their data using more than 100 pre-built operations without needing any coding expertise. Gain insights into the quality and distribution of your data with a variety of integrated charts, graphs, and statistical tools. The tool automatically identifies data types and business classifications, ensuring accuracy and relevance. It also allows easy access to and exploration of data from diverse sources, whether on-premises or cloud-based. Data governance policies set by professionals are automatically enforced within the tool, providing an added layer of compliance. Users can schedule data flow executions for consistent results and easily monitor those results while receiving timely notifications. Furthermore, the solution enables seamless scaling through Apache Spark, allowing transformation recipes to be applied to complete datasets without the burden of managing Apache Spark clusters. This feature enhances efficiency and effectiveness in data processing, making it a valuable asset for organizations looking to optimize their data analytics capabilities.

Oracle Big Data Preparation Cloud Service is a comprehensive managed Platform as a Service (PaaS) solution that facilitates the swift ingestion, correction, enhancement, and publication of extensive data sets while providing complete visibility in a user-friendly environment. This service allows for seamless integration with other Oracle Cloud Services, like the Oracle Business Intelligence Cloud Service, enabling deeper downstream analysis. Key functionalities include profile metrics and visualizations, which become available once a data set is ingested, offering a visual representation of profile results and summaries for each profiled column, along with outcomes from duplicate entity assessments performed on the entire data set. Users can conveniently visualize governance tasks on the service's Home page, which features accessible runtime metrics, data health reports, and alerts that keep them informed. Additionally, you can monitor your transformation processes and verify that files are accurately processed, while also gaining insights into the complete data pipeline, from initial ingestion through to enrichment and final publication. The platform ensures that users have the tools needed to maintain control over their data management tasks effectively.

In today’s constantly connected economy, the volume of data generated is skyrocketing. It’s crucial to adopt a data-driven approach that enables rapid responses and innovations to stay ahead of your rivals. Imagine if you could streamline the processes of data preparation and provisioning. Consider the benefits of conducting database analysis with ease and sharing valuable data insights among analysts across various teams. What if achieving all of this could lead to time savings of up to 40%? When paired with Toad® Data Point, Toad Intelligence Central serves as a budget-friendly, server-based solution that empowers your organization. It enhances collaboration among Toad users by providing secure and governed access to SQL scripts, project artifacts, provisioned data, and automation workflows. Furthermore, it allows for seamless abstraction of both structured and unstructured data sources through advanced connectivity, enabling the creation of refreshable datasets accessible to any Toad user. Ultimately, this integration not only optimizes efficiency but also fosters a culture of data-driven decision-making within your organization.

Self-service analytics tools were designed to empower end-users by enhancing their agility and fostering a data-driven culture. Unfortunately, this boost in agility often resulted in fragmented and isolated workflows due to a lack of data governance, leading to chaotic data management practices. Knowledge Hub offers a solution that effectively tackles these challenges, benefiting business users while simultaneously streamlining and fortifying IT governance. Featuring an easy-to-use browser-based interface, it automates the tasks involved in data transformation, making it the only collaborative data preparation tool available in today's market. This enables business teams to collaborate effortlessly with data engineers and scientists, providing a tailored experience for creating, validating, and sharing datasets and analytical models that are both governed and reliable. With no coding necessary, a wider audience can contribute to collaborative efforts, ultimately leading to better-informed decision-making. Governance, data lineage, and collaboration are seamlessly managed within a cloud-compatible solution specifically designed to foster innovation. Additionally, the platform's extensibility and low- to no-code capabilities empower individuals from various departments to efficiently transform data, encouraging a culture of shared insights and collaboration throughout the organization.

Create, execute, and oversee AI models while enhancing decision-making at scale across any cloud infrastructure. IBM Watson Studio enables you to implement AI seamlessly anywhere as part of the IBM Cloud Pak® for Data, which is the comprehensive data and AI platform from IBM. Collaborate across teams, streamline the management of the AI lifecycle, and hasten the realization of value with a versatile multicloud framework. You can automate the AI lifecycles using ModelOps pipelines and expedite data science development through AutoAI. Whether preparing or constructing models, you have the option to do so visually or programmatically. Deploying and operating models is made simple with one-click integration. Additionally, promote responsible AI governance by ensuring your models are fair and explainable to strengthen business strategies. Leverage open-source frameworks such as PyTorch, TensorFlow, and scikit-learn to enhance your projects. Consolidate development tools, including leading IDEs, Jupyter notebooks, JupyterLab, and command-line interfaces, along with programming languages like Python, R, and Scala. Through the automation of AI lifecycle management, IBM Watson Studio empowers you to build and scale AI solutions with an emphasis on trust and transparency, ultimately leading to improved organizational performance and innovation.