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Databricks

databricks

Do you have a problem like this?

Data is distributed across departments
I don't know where the data is
Although a data integration platform has been introduced,
Slow operation due to increased data
I want to start using AI
I don't know how to tackle it

Next-generation data integration platform "Lakehouse"

Lake House The platform based on
Support all your data and governance requirements Providing an open, integrated environment death,
This enables data processing that is suited to the characteristics of your business.
  • Databricks' "Lakehouse" is a next-generation data integration platform that enables data collection, storage, analysis, AI model development, and operation all in one, efficient, and low-cost manner on a single platform.
  • By centrally managing data in "Lake House," it is possible to utilize data across departments, collaborate among users with different skills, and unify governance.

Lake house features

①Data unification & catalog function
  • Unstructured data,
    Supports batch and stream format data
  • By cataloging the integrated data,
    Clarifying data rights, location, and ownership
②High-speed processing and scalability
  • On major public clouds
    Equipped with a configurable high-speed processing engine
  • High functionality that allows resource expansion with the push of a button
    Realizing a highly scalable data integration platform
③ Utilizing data with "+AI"
  • Not just data collection and visualization (BI)
    Preparing an AI development environment
  • Future development and operation of AI models
    Realized through a single platform

Lake house introduction effect

Before
data complexity
  • Data generated by disorganized systems that is difficult to interact with
  • Data utilization is slow due to different data formats for each department.
Data owner absent
  • Unmaintained data with no owner or administrator​
  • Data permissions and usage regulations are vague, so it takes time to obtain approval for use.
Lack of data utilization personnel
  • A data linkage tool has been introduced, but its utilization is slow due to lack of human resources who can handle it.
After
Unification of data infrastructure
  • Supports many linked services! A unified data utilization platform makes it easy to link with various services!
  • Supports unstructured data and batch stream formats.
Data visualization
With catalog function
  • Clarify data owners and control access to each data
  • You can check the authority and location of data and immediately check whether it can be used or not.
Skill improvement
  • Improve the skills of members in a rich workspace environment

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Case study

Databricks - User Case Study -Yokogawa Electric Corporation Corporation

Adopting Databricks to democratize data within the company Supporting data-driven, flexible and speedy development
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Seminar

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video on demand

ChatGPT vs Open Source LLM, Which Large Language Model to Choose for Internal Knowledge?
ChatGPT vs Open Source LLM, Which Large Language Model to Choose for Internal Knowledge?
With the release of ChatGPT, it has become widely recognized that building large-scale language models can perform a variety of tasks, and an increasing number of companies are introducing ChatGPT to improve business efficiency and utilize internal knowledge. On the other hand, some companies may be hesitant to introduce ChatGPT in-house due to concerns about information leaks. This issue may be solved by building your own LLM using an open source LLM that has already been trained in Japanese. In this video, we will compare the advantages and disadvantages of using in-house knowledge with ChatGPT and open source LLM, and explain how to build your own LLM using actual environments and knowledge data. Please take a look.
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company description

company name Databricks Inc.
Established 2013
location San Francisco, California, United States
representative Ali Ghodsi
website https://www.databricks.com/jp/

Inquiry/Document request

In charge of Macnica Databricks Co., Ltd.

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Weekdays: 9:00-17:00