Best Data Management Software for Mac of 2025 - Page 22

HEAVY.AI is a pioneer in accelerated analysis. The HEAVY.AI platform can be used by government and business to uncover insights in data that is beyond the reach of traditional analytics tools. The platform harnesses the huge parallelism of modern CPU/GPU hardware and is available both in the cloud or on-premise. HEAVY.AI was developed from research at Harvard and MIT Computer Science and Artificial Intelligence Laboratory. You can go beyond traditional BI and GIS and extract high-quality information from large datasets with no lag by leveraging modern GPU and CPU hardware. To get a complete picture of what, when and where, unify and explore large geospatial or time-series data sets. Combining interactive visual analytics, hardware accelerated SQL, advanced analytics & data sciences frameworks, you can find the opportunity and risk in your enterprise when it matters most.

Achieve limitless data retention for Apache Kafka® with Confluent, empowering you to be infrastructure-enabled rather than constrained by outdated systems. Traditional technologies often force a choice between real-time processing and scalability, but event streaming allows you to harness both advantages simultaneously, paving the way for innovation and success. Have you ever considered how your rideshare application effortlessly analyzes vast datasets from various sources to provide real-time estimated arrival times? Or how your credit card provider monitors millions of transactions worldwide, promptly alerting users to potential fraud? The key to these capabilities lies in event streaming. Transition to microservices and facilitate your hybrid approach with a reliable connection to the cloud. Eliminate silos to ensure compliance and enjoy continuous, real-time event delivery. The possibilities truly are limitless, and the potential for growth is unprecedented.

FairCom DB is ideal to handle large-scale, mission critical core-business applications that demand performance, reliability, and scalability that cannot easily be achieved with other databases. FairCom DB provides predictable high-velocity transactions with big data analytics and massively parallel big-data processing. It provides developers with NoSQL APIs that allow them to process binary data at machine speed. ANSI SQL allows for simple queries and analysis over the same binary data. Verizon is one of the companies that has taken advantage of FairCom DB's flexibility. Verizon recently selected FairCom DB to be its in-memory database for the Verizon Intelligent Network Control Platform Transaction Server Migrating. FairCom DB, an advanced database engine, gives you a Continuum of Control that allows you to achieve unparalleled performance at a low total cost of ownership (TCO). FairCom DB doesn't conform to you. FairCom DB conforms. FairCom DB doesn't force you to conform to the database's limitations.

Embiot®, a compact, high-performance IoT analytics software agent that can be used for smart sensor and IoT gateway applications, is available. This edge computing application can be integrated directly into devices, smart sensor and gateways but is powerful enough to calculate complex analytics using large amounts of raw data at high speeds. Embiot internally uses a stream processing model in order to process sensor data that arrives at different times and in different order. It is easy to use with its intuitive configuration language, rich in math, stats, and AI functions. This makes it quick and easy to solve any analytics problems. Embiot supports many input methods, including MODBUS and MQTT, REST/XML and REST/JSON. Name/Value, CSV, and REST/XML are all supported. Embiot can send output reports to multiple destinations simultaneously in REST, custom text and MQTT formats. Embiot supports TLS on select input streams, HTTP, and MQTT authentication for security.

A Business Intelligence platform that seamlessly operates with massively parallel databases designed for data visualization, operational analytics, as well as tracking and reporting capabilities. MPP BI creates a comprehensive pathway to access your business data efficiently. It ensures that all necessary information from various data sources is prepared, cleaned, and consolidated, making it readily available for your tasks. With a diverse array of integration options, the platform is versatile and adaptable to your needs. The processing capabilities are extensive, allowing for limitless combinations of different data types, processing techniques, and update frequencies. For visualization, the platform offers an abundance of fundamental data visuals, along with features like drilling down, ratings, and other user-friendly tools that enhance decision-making. Collaboration is made easy with interactive presentations, analytics that can be natively embedded in your corporate portal, and straightforward data export functionalities. Additionally, the platform includes robust text analytics that uncover significant insights from your text data. Built on top of the MPP BI framework, our fast and scalable text analytical engine, MPP NLP, offers features such as text filtering and both rule-based and machine learning classification, ensuring your data analysis is as effective as possible. By integrating these capabilities, businesses can gain a deeper understanding of their data landscape and make more informed decisions.

Gilhari is a microservice framework that provides persistence for JSON objects in relational database. This microservice framework is available as a Docker image and can be configured according to an app-specific object or relational model. Gilhari exposes REST (Representational State Transfer) interface for APIs (POST/GET, PUT and DELETE ) to perform CRUD (Create. Retrieve. Update. Delete) operations on app-specific JSON objects. Here are some highlights from Gilhari: * Metadata driven, object model independent and database agnostic framework * Easily customizable/configurable to your JSON object model * JSON attributes can be mapped to table columns, allowing full query capabilities as well as optimizations * Supports complex object modeling, including 1-m, 1-m and m-m relationships * No code is required to handle REST APIs (POST/GET, PUT/DELETE), data exchange (CRUD), or database schema creation.

Molecula serves as an enterprise feature store that streamlines, enhances, and manages big data access to facilitate large-scale analytics and artificial intelligence. By consistently extracting features, minimizing data dimensionality at the source, and channeling real-time feature updates into a centralized repository, it allows for millisecond-level queries, computations, and feature re-utilization across various formats and locations without the need to duplicate or transfer raw data. This feature store grants data engineers, scientists, and application developers a unified access point, enabling them to transition from merely reporting and interpreting human-scale data to actively forecasting and recommending immediate business outcomes using comprehensive data sets. Organizations often incur substantial costs when preparing, consolidating, and creating multiple copies of their data for different projects, which delays their decision-making processes. Molecula introduces a groundbreaking approach for continuous, real-time data analysis that can be leveraged for all mission-critical applications, dramatically improving efficiency and effectiveness in data utilization. This transformation empowers businesses to make informed decisions swiftly and accurately, ensuring they remain competitive in an ever-evolving landscape.

JanusGraph stands out as a highly scalable graph database designed for efficiently storing and querying extensive graphs that can comprise hundreds of billions of vertices and edges, all managed across a cluster of multiple machines. This project, which operates under The Linux Foundation, boasts contributions from notable organizations such as Expero, Google, GRAKN.AI, Hortonworks, IBM, and Amazon. It offers both elastic and linear scalability to accommodate an expanding data set and user community. Key features include robust data distribution and replication methods to enhance performance and ensure fault tolerance. Additionally, JanusGraph supports multi-datacenter high availability and provides hot backups for data security. All these capabilities are available without any associated costs, eliminating the necessity for purchasing commercial licenses, as it is entirely open source and governed by the Apache 2 license. Furthermore, JanusGraph functions as a transactional database capable of handling thousands of simultaneous users performing complex graph traversals in real time. It ensures support for both ACID properties and eventual consistency, catering to various operational needs. Beyond online transactional processing (OLTP), JanusGraph also facilitates global graph analytics (OLAP) through its integration with Apache Spark, making it a versatile tool for data analysis and visualization. This combination of features makes JanusGraph a powerful choice for organizations looking to leverage graph data effectively.

Cayley is an open-source database tailored for Linked Data, drawing inspiration from the graph database that supports Google's Knowledge Graph, previously known as Freebase. This graph database is crafted for user-friendliness and adept at handling intricate data structures, featuring an integrated query editor, a visualizer, and a Read-Eval-Print Loop (REPL). It supports various query languages, including Gizmo, which is influenced by Gremlin, a GraphQL-like query language, and MQL, a streamlined version catering to Freebase enthusiasts. Cayley's modular architecture allows seamless integration with preferred programming languages and backend storage solutions, making it production-ready, thoroughly tested, and utilized by numerous companies for their operational tasks. Additionally, it is optimized for application use, demonstrating impressive performance metrics; for instance, testing has shown that it can effortlessly manage 134 million quads in LevelDB on consumer-grade hardware from 2014, with multi-hop intersection queries—such as finding films featuring both X and Y—executing in about 150 milliseconds. By default, Cayley is set up to operate in-memory, which is what the backend memstore refers to, thereby enhancing its speed and efficiency for data retrieval and manipulation. Overall, Cayley offers a powerful solution for those looking to leverage linked data in their applications.

Sparksee, which was previously referred to as DEX, optimizes both space and performance while maintaining a compact design that enables swift analysis of extensive networks. It supports a wide range of programming languages including .Net, C++, Python, Objective-C, and Java, making it versatile across various operating systems. The graph data is efficiently organized using bitmap data structures, achieving significant compression ratios. These bitmaps are divided into chunks that align with disk pages, enhancing input/output locality for better performance. By leveraging bitmaps, computations are executed using binary logic instructions that facilitate efficient processing in pipelined architectures. The system features complete native indexing, which ensures rapid access to all graph data structures. Node connections are also encoded as bitmaps, further reducing their storage footprint. Advanced I/O strategies are implemented to minimize the frequency of data pages being loaded into memory, ensuring optimal resource usage. Each unique value in the database is stored only once, effectively eliminating unnecessary redundancy, and contributing to overall efficiency. This combination of features makes Sparksee a powerful tool for handling large-scale graph data analyses.

Power Query provides a user-friendly solution for connecting, extracting, transforming, and loading data from a variety of sources. Acting as a robust engine for data preparation and transformation, Power Query features a graphical interface that simplifies the data retrieval process and includes a Power Query Editor for implementing necessary changes. The versatility of the engine allows it to be integrated across numerous products and services, meaning the storage location of the data is determined by the specific application of Power Query. This tool enables users to efficiently carry out the extract, transform, and load (ETL) processes for their data needs. With Microsoft’s Data Connectivity and Data Preparation technology, users can easily access and manipulate data from hundreds of sources in a straightforward, no-code environment. Power Query is equipped with support for a multitude of data sources through built-in connectors, generic interfaces like REST APIs, ODBC, OLE, DB, and OData, and even offers a Power Query SDK for creating custom connectors tailored to individual requirements. This flexibility makes Power Query an indispensable asset for data professionals seeking to streamline their workflows.

DataPreparator is a complimentary software application aimed at facilitating various aspects of data preparation, also known as data preprocessing, within the realms of data analysis and mining. This tool provides numerous functionalities to help you explore and ready your data before engaging in analysis or mining activities. It encompasses a range of features including data cleaning, discretization, numerical adjustments, scaling, attribute selection, handling missing values, addressing outliers, conducting statistical analyses, visualizations, balancing, sampling, and selecting specific rows, among other essential tasks. The software allows users to access data from various sources such as text files, relational databases, and Excel spreadsheets. It is capable of managing substantial data volumes effectively, as datasets are not retained in computer memory, except for Excel files and the result sets from certain databases that lack data streaming support. As a standalone tool, it operates independently of other applications, boasting a user-friendly graphical interface. Additionally, it enables operator chaining to form sequences of preprocessing transformations and allows for the creation of a model tree specifically for test or execution data, thereby enhancing the overall data preparation process. Ultimately, DataPreparator serves as a versatile and efficient resource for those engaged in data-related tasks.

MySQL Workbench serves as an integrated visual platform designed for database architects, developers, and administrators. It encompasses functionalities for data modeling, SQL development, and a wide range of administrative tasks like server setup, user management, and backup solutions. Compatible with Windows, Linux, and Mac OS X, MySQL Workbench allows users to visually design and manage databases efficiently. This tool provides everything necessary for data modelers to create intricate ER models while also facilitating forward and reverse engineering processes. Additionally, it offers essential features for managing changes and documentation, which typically consume considerable time and resources. With visual tools for building, executing, and refining SQL queries, MySQL Workbench enhances productivity. The SQL Editor boasts features such as syntax highlighting, auto-completion, the ability to reuse SQL snippets, and a history of SQL executions for easy tracking. Furthermore, the Database Connections Panel streamlines the management of database connections, making it user-friendly for developers at all levels.

Postico serves as a user-friendly database client that allows users to connect to a local PostgreSQL server hosted on their Mac or to remote servers on different machines. For those looking to set up a local PostgreSQL server on their Mac, Postgres.app is highly recommended as it efficiently initiates a PostgreSQL server locally. Upon its first launch, Postgres.app automatically generates a new data directory and sets up an empty database, swiftly getting the server ready to accept connections. However, it's important to note that Postgres.app cannot operate if another PostgreSQL server is already active on your machine, so if you encounter any issues, consider deactivating or uninstalling any other PostgreSQL installations. After uninstalling, it’s advisable to reboot your computer to ensure that all changes take effect. To establish a connection to a PostgreSQL server using Postico, you will need to create a favorite, which stores the necessary connection parameters; while some of these parameters are optional, they can enhance the connection experience. In creating a favorite, you can ensure a smoother and more efficient connection process.

Sedna is an open-source native XML database that offers a comprehensive suite of fundamental database functionalities, such as persistent storage, ACID transactions, security measures, indexing, and hot backups. It boasts adaptable XML processing capabilities, featuring a W3C XQuery implementation that is seamlessly integrated with full-text search options and a node-level update syntax. Users can access several straightforward examples that are executable directly from the command line, alongside detailed instructions on how to execute the provided examples with Sedna. The distribution of Sedna includes a set of examples centered around the XMark XML benchmark, which facilitates easy exploration of Sedna's features. Among these examples are processes for bulk loading a sample XML document and executing various sample XQuery queries and updates on it. In the following section, we will demonstrate how to execute one of these examples effectively. Additionally, this user-friendly approach ensures that both beginners and experienced users can quickly grasp the functionalities available within Sedna.

LevelDB is a high-performance key-value storage library developed by Google, designed to maintain an ordered mapping between string keys and string values. The keys and values are treated as arbitrary byte arrays, and the stored data is organized in a sorted manner based on the keys. Users have the option to supply a custom comparison function to modify the default sorting behavior. The library allows for multiple changes to be grouped into a single atomic batch, ensuring data integrity during updates. Additionally, users can create a temporary snapshot for a consistent view of the data at any given moment. The library supports both forward and backward iteration through the stored data, enhancing flexibility during data access. Data is automatically compressed using the Snappy compression algorithm to optimize storage efficiency. Moreover, interactions with the external environment, such as file system operations, are managed through a virtual interface, giving users the ability to customize how the library interacts with the operating system. In practical applications, we utilize a database containing one million entries, where each entry consists of a 16-byte key and a 100-byte value. Notably, the values used in benchmarking compress to approximately half of their original size, allowing for significant space savings. We provide detailed performance metrics for sequential reads in both forward and reverse directions, as well as the effectiveness of random lookups, to showcase the library's capabilities. This comprehensive performance analysis aids developers in understanding how to optimize their use of LevelDB in various applications.

Data Loader serves as a client application designed for the efficient bulk management of data, allowing users to import or export records within Salesforce. It facilitates tasks such as inserting, updating, deleting, or exporting data effectively. When handling data imports, Data Loader reads and extracts information from CSV files or connects directly to a database to load the necessary data. Conversely, for data exports, it generates output in the form of CSV files. The user interface enables interactive configuration, allowing users to define parameters, select CSV files for import or export, and establish field mappings that align the field names from the import files with those in Salesforce. The application also features drag-and-drop capabilities for field mapping, ensuring a user-friendly experience. Additionally, Data Loader supports all object types, including custom objects, making it a versatile tool for data management.

Rsync is a freely available open source tool that enables quick incremental file transfers and is distributed under the GNU General Public License. Users can obtain the GPG signing key for the release files from public PGP key servers; if automatic key-fetching is activated, simply executing a "gpg --verify" command will automatically retrieve the key. Alternatively, individuals may choose to manually download the GPG key associated with Wayne Davison. Designed primarily for Unix systems, rsync employs a unique "rsync algorithm" that allows for efficient synchronization of remote files by transmitting only the differences between them, rather than requiring complete sets of files to be present at either end of the connection. Additionally, rsync can optionally maintain the integrity of symbolic links, hard links, file ownership, permissions, devices, and timestamps. With its internal pipelining feature, rsync significantly reduces latency when processing multiple files, making it an optimal choice for users seeking effective file transfer solutions. Overall, rsync stands out as a powerful and versatile tool for efficient file management across different systems.

With a variety of preset modules and low-code editing features readily accessible, you have everything necessary for effective data management. Harness the capabilities of both Windows and Mac to design your ideal ERP solution, all without relying on cloud services. The Android and iOS applications ensure that your databases remain at your fingertips, even when offline. You can browse using your preferred web browsers from any location, making accessibility a breeze. Each template comes equipped with numerous components that are ready for installation, tailored to fit your workflow seamlessly. User permissions allow for the assignment of specific data to one or more individuals, ensuring that each user can access only their designated information alongside what you choose to share. Nios4 even supports operation on a single device without the need for data sharing, although doing so may limit your experience of the system's full capabilities. You can create as many sections as needed and optimize how your data is displayed in tables. Additionally, the integrated GPS functionality enables you to pinpoint the exact locations where your data is gathered, enhancing your data collection efforts. This comprehensive approach to data management ensures that you remain organized and efficient, regardless of your working environment.

Nowadays, many organizations are increasingly utilizing object and cloud storage to hold unstructured data, in addition to traditional file systems. The benefits of cloud and object storage, especially for inactive data, have prompted a significant migration or replication of files from legacy NAS systems to these modern solutions. This shift has resulted in a growing amount of data being housed in cloud and object storage; however, it has also introduced an often-overlooked security vulnerability. Typically, the data stored in cloud services or on-premises object storage remains unbacked up due to the common misconception that it is inherently secure. Such an assumption is both negligent and fraught with risk, as the high availability and redundancy provided by these services do not safeguard against issues like human error, ransomware attacks, malware infections, or technology failures. Therefore, it is crucial to implement backup or replication strategies for data kept in cloud and object storage, ideally using a different storage technology located elsewhere, and retaining the original format as it exists in the cloud. By doing so, organizations can enhance their data protection measures and mitigate potential threats to their valuable information.

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.

Harness the capabilities of both private and public clouds through a unique hybrid data platform tailored for contemporary data architectures, enabling data access from any location. Cloudera stands out as a hybrid data platform that offers unparalleled flexibility, allowing users to choose any cloud, any analytics solution, and any type of data. It streamlines data management and analytics, ensuring optimal performance, scalability, and security for data accessibility from anywhere. By leveraging Cloudera, organizations can benefit from the strengths of both private and public clouds, leading to quicker value realization and enhanced control over IT resources. Moreover, Cloudera empowers users to securely transfer data, applications, and individuals in both directions between their data center and various cloud environments, irrespective of the data's physical location. This bi-directional capability not only enhances operational efficiency but also fosters a more adaptable and responsive data strategy.

Introducing revolutionary data streaming features that enable unparalleled customer experiences. The Kafka API and its ecosystem are fully compatible with Redpanda, which boasts predictable low latencies and ensures zero data loss. Redpanda is designed to outperform Kafka by up to ten times, offering enterprise-level support and timely hotfixes. It also includes automated backups to S3 or GCS, providing a complete escape from the routine operations associated with Kafka. Additionally, it supports both AWS and GCP environments, making it a versatile choice for various cloud platforms. Built from the ground up for ease of installation, Redpanda allows for rapid deployment of streaming services. Once you witness its incredible capabilities, you can confidently utilize its advanced features in a production setting. We take care of provisioning, monitoring, and upgrades without requiring access to your cloud credentials, ensuring that sensitive data remains within your environment. Your streaming infrastructure will be provisioned, operated, and maintained seamlessly, with customizable instance types available to suit your specific needs. As your requirements evolve, expanding your cluster is straightforward and efficient, allowing for sustainable growth.

eMite serves as a comprehensive operational intelligence platform that integrates sophisticated analytics, data correlation, KPI management, and threshold alerting into a single, ready-to-use solution accessible via a web browser, facilitating actionable insights from both current and archival data. The platform boasts a highly adaptable and robust ETL (extract, transform, load) framework, utilizing various technologies such as APIs, XML, JSON, and SQL for effective data extraction. With over 80 pre-built adaptors, eMite seamlessly integrates data from popular third-party applications provided by companies like Salesforce, Microsoft, Oracle, Atlassian, Snare, and Genesys. Additionally, it accommodates data onboarding from more generic sources, including databases and Excel files, enhancing its versatility. Furthermore, eMite features a KPI management system that empowers users to create tailored KPIs that align with their specific operational needs, thus optimizing performance tracking and decision-making processes. This level of customization ensures that organizations can effectively monitor their unique metrics and improve their overall efficiency.