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Databricks

databricks

Do you have a problem like this?

Data integration infrastructure has not been introduced...
  • Data is distributed by department, and what data is located where?
    I don't know
  • The operational load of multiple systems is high.
    Unable to collaborate
  • Data formats vary by department, making cross-sectional utilization difficult.
Although we have already introduced a data integration platform...
  • Due to the increase in the amount of data, the operation becomes slow and difficult to use.
  • I want to use new data, but the data format and processing method are different.
    cannot be integrated
  • It is difficult to expand resources and operate the system in preparation for the increase in data.

Next-generation data integration platform "Lakehouse"

“Lakehouse” provided by Databricks is a Data collection, accumulation, analysis, AI model development and operation can be implemented all at once, efficiently, and at low cost.
This is a next-generation data integration 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
  • Supports unstructured data and batch/stream data
  • Clarify data authority, location, and owner by cataloging integrated data
② Realize “+AI” data utilization
  • In addition to data collection and visualization (BI), we also provide an AI development environment.
  • Future AI model development and operation can also be realized on a single platform
③High-speed processing & scalable
  • Equipped with a high-speed processing engine that can be built on major public cloud services
  • Realizes a highly functional and scalable data integration platform that allows resource expansion with the touch of a button.

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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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