Best Data Management Software for Scala

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

Parasoft's mission is to provide automated testing solutions and expertise that empower organizations to expedite delivery of safe and reliable software. A powerful unified C and C++ test automation solution for static analysis, unit testing and structural code coverage, Parasoft C/C++test helps satisfy compliance with industry functional safety and security requirements for embedded software systems.

The Archon Data Store™ is a robust and secure platform built on open-source principles, tailored for archiving and managing extensive data lakes. Its compliance capabilities and small footprint facilitate large-scale data search, processing, and analysis across structured, unstructured, and semi-structured data within an organization. By merging the essential characteristics of both data warehouses and data lakes, Archon Data Store creates a seamless and efficient platform. This integration effectively breaks down data silos, enhancing data engineering, analytics, data science, and machine learning workflows. With its focus on centralized metadata, optimized storage solutions, and distributed computing, the Archon Data Store ensures the preservation of data integrity. Additionally, its cohesive strategies for data management, security, and governance empower organizations to operate more effectively and foster innovation at a quicker pace. By offering a singular platform for both archiving and analyzing all organizational data, Archon Data Store not only delivers significant operational efficiencies but also positions your organization for future growth and agility.

Recognized worldwide for excellence, AnyChart is an award-winning JavaScript (HTML5) library that empowers teams to transform raw data into engaging visual experiences. Built to serve the needs of both developers and businesses, it provides a complete toolkit for creating interactive charts, maps, and dashboards that perform reliably across web, mobile, and desktop environments. With a robust set of over 90 chart types — including Gantt, stock, geospatial, bar, and line — AnyChart makes it easy to turn complex information into clear, actionable insights. Its seamless integration with any technology stack and compatibility with diverse data sources make it ideal for enhancing reports, powering embedded analytics in SaaS platforms, or supporting enterprise-grade systems. Fully customizable, responsive, and continuously evolving with new features, AnyChart delivers simplicity, flexibility, and fast, high-quality results. Trusted by more than 75% of Fortune 500 companies and thousands of developers all over the world, it is the proven choice for advanced data visualization. Get started with AnyChart JS Charts today!

Ably is the definitive realtime experience platform. We power more WebSocket connections than any other pub/sub platform, serving over a billion devices monthly. Businesses trust us with their critical applications like chat, notifications and broadcast - reliably, securely and at serious scale.

Datalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time.

Traditional Data Lakes frequently simplify their role to merely serving as inexpensive raw data repositories, overlooking crucial elements such as data transformation, quality assurance, and security protocols. Consequently, data scientists often find themselves dedicating as much as 80% of their time to the processes of data acquisition, comprehension, and cleansing, which delays their ability to leverage their primary skills effectively. Furthermore, the establishment of traditional Data Lakes tends to occur in isolation by various departments, each utilizing different standards and tools, complicating the implementation of cohesive analytical initiatives. In contrast, Smart Data Lakes address these challenges by offering both architectural and methodological frameworks, alongside a robust toolset designed to create a high-quality data infrastructure. Essential to any contemporary analytics platform, Smart Data Lakes facilitate seamless integration with popular Data Science tools and open-source technologies, including those used for artificial intelligence and machine learning applications. Their cost-effective and scalable storage solutions accommodate a wide range of data types, including unstructured data and intricate data models, thereby enhancing overall analytical capabilities. This adaptability not only streamlines operations but also fosters collaboration across different departments, ultimately leading to more informed decision-making.

DataWorks, a comprehensive Big Data platform introduced by Alibaba Cloud, offers an all-in-one solution for Big Data development, management of data permissions, offline job scheduling, and more. The platform is designed to function seamlessly right from the start, eliminating the need for users to manage complex underlying clusters and operations. Users can effortlessly build workflows through a drag-and-drop interface, while also having the ability to edit and debug their code in real-time, inviting collaboration from fellow developers. The platform supports a wide range of functionalities, including data integration, MaxCompute SQL, MaxCompute MR, machine learning, and shell tasks. Additionally, it features robust task monitoring capabilities, providing alerts in case of errors to prevent service disruptions. With the ability to run millions of tasks simultaneously, DataWorks accommodates various scheduling options, including hourly, daily, weekly, and monthly tasks. As an exceptional platform for constructing big data warehouses, DataWorks delivers extensive data warehousing services, catering to all aspects of data aggregation, processing, governance, and services. Its user-friendly design and powerful features make it an indispensable tool for organizations looking to harness the power of Big Data effectively.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

Keep a close eye on your data health and the performance of your pipelines. Achieve comprehensive oversight for pipelines utilizing cloud-native technologies such as Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability platform is specifically designed for Data Engineers. As the challenges in data engineering continue to escalate due to increasing demands from business stakeholders, Databand offers a solution to help you keep pace. With the rise in the number of pipelines comes greater complexity. Data engineers are now handling more intricate infrastructures than they ever have before while also aiming for quicker release cycles. This environment makes it increasingly difficult to pinpoint the reasons behind process failures, delays, and the impact of modifications on data output quality. Consequently, data consumers often find themselves frustrated by inconsistent results, subpar model performance, and slow data delivery. A lack of clarity regarding the data being provided or the origins of failures fosters ongoing distrust. Furthermore, pipeline logs, errors, and data quality metrics are often gathered and stored in separate, isolated systems, complicating the troubleshooting process. To address these issues effectively, a unified observability approach is essential for enhancing trust and performance in data operations.

Coordinate, explore, and oversee all projects within your data science team efficiently. With Zepl's advanced search functionality, you can easily find and repurpose both models and code. The enterprise collaboration platform provided by Zepl allows you to query data from various sources like Snowflake, Athena, or Redshift while developing your models using Python. Enhance your data interaction with pivoting and dynamic forms that feature visualization tools such as heatmaps, radar, and Sankey charts. Each time you execute your notebook, Zepl generates a new container, ensuring a consistent environment for your model runs. Collaborate with teammates in a shared workspace in real time, or leave feedback on notebooks for asynchronous communication. Utilize precise access controls to manage how your work is shared, granting others read, edit, and execute permissions to facilitate teamwork and distribution. All notebooks benefit from automatic saving and version control, allowing you to easily name, oversee, and revert to previous versions through a user-friendly interface, along with smooth exporting capabilities to Github. Additionally, the platform supports integration with external tools, further streamlining your workflow and enhancing productivity.

The intelligent semantic layer merges data with its business context and interconnections, consolidates metrics, and speeds up the production of data products by allowing for SQL queries that are 90% shorter. Users can easily model the data using familiar business terminology, creating a shared understanding and aligning the metrics with business objectives. By defining semantic relationships that replace traditional JOIN operations, queries become significantly more straightforward. Hierarchies and classifications are utilized to enhance data comprehension. The system automatically aligns data with the semantic model, enabling the integration of various data sources through a robust distributed SQL engine that supports large-scale querying. Data can be accessed as an interconnected semantic graph, improving performance while reducing computing expenses through an advanced caching engine and materialized views. Users gain from sophisticated query optimization techniques. Additionally, Timbr allows connectivity to a wide range of cloud services, data lakes, data warehouses, databases, and diverse file formats, ensuring a seamless experience with your data sources. When executing a query, Timbr not only optimizes it but also efficiently delegates the task to the backend for improved processing. This comprehensive approach ensures that users can work with their data more effectively and with greater agility.