databricks

JULY 8, 2024

The Prodvana team joins Databricks to support new innovations in the Data Intelligence Platform infrastructure. Learn more about the vision and what's ahead.

Data 126

Data 126

Snowflake

JULY 8, 2024

Regulated and sovereign markets across the world have stringent requirements stipulating certain important data be kept within geographical borders or even for certain workloads to have dedicated environments, separate from those of other customers. In these markets, organizations need a secure and well-governed data foundation with effective controls to help comply with regulatory requirements.

Cloud 101

Cloud Google Cloud AWS Education 101

databricks

JULY 8, 2024

We are proud to announce two new analyst reports recognizing Databricks in the data engineering and data streaming space: IDC MarketScape: Worldwide Analytic.

Data Pipeline 114

Data Pipeline Process Cloud Data Engineering 114

KDnuggets

JULY 8, 2024

Text preprocessing is an important step in NLP. Let's learn how to use the Hugging Face Tokenizers Library to preprocess text data.

Data 122

Data 122

Speaker: Jay Allardyce

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Data

Scott Logic

JULY 8, 2024

In this episode, I’m joined by Doro Hinrichs and Kira Clark from Scott Logic and Peter Gostev, Head of AI at Moonpig. Together, we explore whether we can ever really trust and secure Generative AI (GenAI), while sharing stories from the front line about getting to grips with this rapidly evolving technology. With its human-like, non-deterministic nature, GenAI frustrates traditional pass/fail approaches to software testing.

Designing 59

Designing Technology Project IT 59

KDnuggets

JULY 8, 2024

Docker tags are important for managing and versioning Docker images. This tutorial will teach you how to use Docker tags effectively.

Management 106

Management Data Engineering Data Engineer Engineering 106

KDnuggets

JULY 8, 2024

Learn all about introductory statistics with this collection of tutorials from our sister site Statology.

Data Science 102

Data Science Data 102

Hevo

JULY 8, 2024

Managing infrastructure manually across multiple cloud providers leads to consistency, deployment delays, and difficulty in scaling. You need a solution that automates infrastructure provisioning, ensures consistency, and supports rapid deployment across diverse environments, from development to production, while maintaining security and compliance standards.

Cloud 52

Cloud Management Data 52

KDnuggets

JULY 8, 2024

Check out these 6 courses to get your foot into the computer science world!

Computer Science 88

Computer Science 88

Towards Data Science

JULY 8, 2024

Leveraging TensorFlow Transform for scaling data pipelines for production environments Photo by Suzanne D. Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. Tensorflow Transform helps us achieve it in a distributed environment over a huge dataset. Before going further into Data Transformation, Data Validation is the first step of the production pipeline process, which has been covered in my article Validating Data in a Production Pipeline: The

Data Preparation 46

Data Preparation Datasets Metadata Data Ingestion 46

Speaker: Nikhil Joshi, Founder & President of Snic Solutions

In an era where efficiency is king, are you leveraging the right tools to transform your manufacturing processes? A Manufacturing Execution System (MES) is critical for enhancing operational efficiency, reducing waste, and optimizing energy usage—key factors for improving your bottom line and lowering your carbon footprint. Join Nikhil Joshi, a manufacturing technology expert with 18+ years of hands-on experience, in this new webinar as he uncovers the secrets of MES and how to best utilize thes

Manufacturing

Monte Carlo

JULY 8, 2024

Unlike traditional data quality solutions, Monte Carlo was originally designed to reduce data downtime across modern data platforms such as Snowflake, Databricks, Redshift, BigQuery, Azure Synapse and more. As we worked with data teams, we ran into a diverse set of data platforms teams used to power their data products including: Postgres Teradata MySQL Oracle SAP HANA SQL Server Last year we launched custom monitors , or data tests, for these environments to help identify bad data as early in t

SQL 40

SQL MySQL Relational Database Database 40

Hevo

JULY 8, 2024

In the fast-paced world of data management, choosing the right tool to address your requirements has never been more critical than now. Therefore, understanding each option is central to influencing your decisions. This guide compares Hevo Data vs Fivetran, two leading ELT (Extract, Load, Transform) tools in the market.

Data Management 40

Data Management Management Data Data Pipeline 40

Monte Carlo

JULY 8, 2024

Note: This was originally published on the Mission Lane Tech Blog and has been republished below with permission. Table of Contents Introduction Why we started: “Customer obsessed” coupled with cost savings A quick introduction to a traditional compliance testing approach How manual testing works in practice and its drawbacks High Level of Effort Lack of Scalability Testing is not “always on” Less Auditable The future is now!

Data Warehouse 52

Data Warehouse SQL Coding Architecture 52

Precisely

JULY 8, 2024

Key Takeaways: Harness the power of location intelligence to drive smarter, data-driven decisions that turn spatial data into a strategic asset. Top location intelligence use cases include efficient territory planning and network optimization, which help maximize productivity and customer satisfaction. Successful location intelligence initiatives require a foundation of high-quality address data, enrichment data, and spatial analytics.

Telecommunication 63

Telecommunication Insurance Retail Banking 63

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

Systems