Mercari transformed marketing and sales with Google BigQuery and Flywheel Software by applying Machine Learning to user growth and sales growth.

Generative AI and large language models (LLMs) are revolutionizing many aspects of both developer and non-coder productivity with automation of repetitive tasks and fast generation of insights from large amounts of data. Snowflake users are already taking advantage of LLMs to build really cool apps with integrations to web-hosted LLM APIs using external functions, and using Streamlit as an interactive front end for LLM-powered apps such as AI plagiarism detection, AI assistant, and MathGPT.

Cloudera SQL Stream builder gives non-technical users the power of a unified stream processing engine so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. A dead letter queue (DLQ) can be used if there are deserialization errors when events are consumed from a Kafka topic.

CIOs are fed up with having a plethora of BI and analytics tools with business units seemingly chasing the shiniest new solution. And although most industry surveys show data and analytics budgets continuing to grow despite a faltering economy, there is closer scrutiny and belt tightening to rid teams of overlapping capabilities. Here’s a look at how BI tool portfolios have become such a mess and how to streamline them.

Much like Apple people tend to be all Apple, all the time, Microsoft Dynamics ERP users tend to prefer Microsoft products for all their computing needs. It’s not hard to understand why. Using products from the same ecosystem prevents compatibility issues and saves time in learning multiple systems.

Welcome to the third blog post in our series highlighting Snowflake’s data ingestion capabilities, covering the latest on Snowpipe Streaming (currently in public preview) and how streaming ingestion can accelerate data engineering on Snowflake.

Data has become an essential driver for new monetization initiatives in the financial services industry. With the vast amount of data collected from customers, transactions, and market movements, among other sources, this abundance offers tremendous potential for financial institutions to extract valuable insights that can inform business decisions, improve customer service, and create new revenue streams.

If you work in a finance team within a construction business, it’s likely your main goals are to reduce risk, improve profitability, and maintain exceptional levels of compliance. To achieve success, you need direct access to accurate data from your ERP and the ability to quickly create drillable Excel reports for GL and other finance requirements.

For years data and analytics buyers have been managing a difficult set of tradeoffs. They know they need to invest in solutions to drive their businesses and data strategies forward. In fact, every third-party survey shows they plan to do so well into the future. However, customers have struggled in how to balance cost and value, and getting a clear answer isn’t always obvious or easy.

Manufacturers today are implementing a range of new technologies to increase operational efficiency and create visibility and flexibility across value chains. These include robotics, automation, data analytics, IoT, and artificial intelligence (AI) and machine learning (ML), according to Deloitte. Company leaders hope these innovations will help them create more productive and resilient supply chains, improve production quality and efficiency, and mitigate risks.