Macnica begins handling machine learning integrated platform "Databricks" - Helping companies promote the use of AI by eliminating data silos with Lakehouse -

Macnica (Headquarters: Yokohama City, Kanagawa Prefecture, President: Kazumasa Hara, hereinafter Macnica), a total Services & Solutions provider in semiconductors, networks, cybersecurity, and AI/IoT, is a data & AI company that provides the Lakehouse platform. We are pleased to announce that we have entered into a sales partnership with Databricks Inc. (Headquarters: San Francisco, Co-founder & CEO: Ali Ghodsi, hereinafter referred to as Databricks), a leading company in the industry.

Issues of data storage in AI utilization

The adoption of AI for data utilization is gaining momentum in many companies and organizations. In particular, machine learning, which is attracting attention for its use in business, can be used to increase sales through individualized optimization and recommendation functions for customers (optimization of advertisements and recommendations), and to optimize demand forecasts and supply chains (cost reduction, delivery time reduction, etc.). ), predictive maintenance and anomaly detection using IoT and other sensor data. Efficient capture, analysis, and visualization of the data to be analyzed are said to be essential for realizing AI utilization in business. However, if you introduce a "data warehouse" (data storage for storing processed data) for simple data utilization, the already processed data in the storage cannot be used as an analysis target for AI utilization. . On the other hand, in the "data lake" (data storage that stores the original data as it is as big data), which is a platform for AI utilization, specialized knowledge and man-hours are required to process and utilize unprocessed data. is required.
Even with the same data, it is necessary to change the storage destination and storage method due to differences in utilization policies, so many companies and organizations are increasingly fragmenting, duplicating, and siloing data, which is a major issue that hinders the utilization of big data and AI. This is the current situation.

Lakehouse option

Lakehouse is a solution that solves corporate issues in utilizing such data and AI. As an integrated platform that combines the best elements of a data warehouse and a data lake and can store both processed and unprocessed data in one place, it is expected to help promote data affinity in machine learning. Collecting. Among them, Databricks, which we will start handling this time, has three main characteristics.

1. Supports various data types from unstructured data to structured data Databricks can store various unstructured/structured data (text, image, video, audio data, etc.). These data can be used as SaaS from the Data Bricks web screen.

2. With multi-cloud support and open architecture databricks, users can flexibly select and adjust their environment. Users can choose the most cost-effective environment from leading public cloud platforms and use only the resources they need. All components of the platform are open source, so vendor lock-in is avoided, and flexible architecture construction and future changes are possible without relying on specific technologies.

3. Supports various workloads We support workloads in various fields such as data science, machine learning, and SQL, which is a database language. Multiple tools may be required to perform all these workloads, but they all use the same data repository.

<Architecture of Databricks>

At Macnica, in addition to the GPU (Graphical Processing Unit, image processing equipment) products and machine learning model creation tools AI analysis solutions that we have been handling, by providing Databrix this time, we will also be covering AI platforms. We will support the further utilization of AI.

<Inquiries regarding this service>
Macnica Product Manager
E-mail: databricks-sales@macnica.co.jp
URL: https://www.macnica.co.jp/business/dx/manufacturers/databricks/index.html

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