Hyperscale data warehouse supplier Ocient has surveyed enterprise execs and found that AI spending is being prioritized against a background of concern about cloud costs and energy consumption.
Ocient surveyed more than 500 IT and data leaders managing data workloads of 150 terabytes or more in its third annual Beyond Big Data report, titled “Reaching New Altitudes.”
CEO and co-founder Chris Gladwin stated: “As data volumes continue to explode, enterprises are facing a dual challenge: rising costs and growing energy consumption. To harness the full potential of data while mitigating these risks, leaders must fundamentally rethink their data analytics strategies. The latest Beyond Big Data report confirms this shift and highlights the critical significance of sustainable, energy-efficient data analytics and management solutions.”
The headline findings include:
- 100 percent of IT and data leaders agree that increasing the volume of data analyzed by their organization near-term is important.
- Only 25 percent of respondents report prioritizing cloud-only data analytics solutions in the next 12-18 months, a 10 percent drop from 2023.
- While data speed and security remain crucial, sustainable energy consumption and cost have emerged as top priorities.
- 64 percent of enterprise leaders cite “surprising” cloud costs as the leading factor impacting their ability to predict spend accurately.
- More than half (53 percent) of respondents say energy consumption is a top concern.
- Nearly a third (31 percent) cite reducing energy consumption as a motivator to switch or upgrade data warehouse or database solutions.
- 93 percent of IT and data leaders plan to make AI investments in the next 12-18 months.
Ocient compared its 2023 and 2024 survey results and found four key changes in responses:
It acknowledges that AI, specifically machine learning (ML), has become an essential way to analyze vast data stores and is offering ML built directly into its hyperscale data warehouse with its OcientML offering.
Its machine learning support is, we’re told, extensive, interoperable, and built for data scientists. They can use SQL commands to work directly in the database and also integrate OcientML with third-party tools like Jupyter notebooks to train models and run predictions on datasets in the Ocient system.
Ocient maintains a repository of common ML models that can be used as written or adapted to meet specific needs, the company says. It’s focused on algorithms commonly seen in structured and semi-structured data that run on features that have terabytes or more of data. It includes many regression and classification models, as well as feed-forward neural network models, principal component analysis models, and more.
Find a complete list of OcientML models and their definitions here.
No data movement out of Ocient’s repository is needed to run ML models, and users can use full resolution data to build their models, iterating rapidly without worrying about impacting other workloads, Ocient says.
The company adds that customers can generate powerful machine learning models with the simplicity of SQL, in-database machine learning training, and prediction built directly into the database engine. This, Ocient says, streamlines ML and saves developers time. They can create and train ML models within the database using CREATE MLMODEL statement with a SELECT to specify independent and dependent variables.
Then they can query using MLMODEL name as a function in a SELECT statement specifying one or more columns as independent variables in the MLMODEL.
Models within the Ocient Hyperscale Data Warehouse are first-class database objects, created with data definition language (DDL) and accessible with SQL. Training requires a SQL statement. For example, the SQL command for creating a simple linear regression model might look like this:
Once the model has been created, it runs a prediction as you put the model name in a Select statement and use it as you would any function:
SELECT my_model(col1) FROM my_table;
This can be done when creating new tables, when inserting into new tables, or when running a query.
Ocient does not support generative AI with large language models and a focus on ordinary users as its ML tools are instead targeted toward data scientists. A search on Ocient’s website for “generative AI” returned “no results found.”
Ocient would like Apache Druid, Snowflake, AWS Redshift Aqua, and other data warehouse users to switch to its data platform because it claims it is better at scaling, faster, more energy-efficient, and offers better value for money.