phData

DECEMBER 16, 2024

About phData phData, one of the largest pure-play data engineering companies globally, is certified as a Snowflake Elite Services Partner and an AWS Advanced Consulting Partner. Specializing in AI and data applications, phData offers services including data engineering, AI & machine learning , and analytics & visualization.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Smart Data Collective

JULY 25, 2019

The proprietary technologies they use cuts down the time required to come to conclusions and allow the users to view more data when evaluating a client. It has an AI data engine that gathers information from multiple sources, like government data sets and news articles. billion merger with Cloudera.

Big Data 107

Big Data Big Data AI AI 107

Data Science Blog

JUNE 27, 2023

Kappa – Architecture Jay Kreps introduced the Kappa architecture in 2014 as an alternative to the Lambda architecture. However, it is worth mentioning that while the batch layer and real-time stream handle different scenarios, their underlying processing logic often shares similarities.

Big Data 130

Big Data Big Data Apache Kafka Database 130

phData

JULY 12, 2024

This recognition holds significant meaning for us as it reflects our continuous growth since our establishment in 2014. What Our Employees Love About phData Since 2014, the phData team has emphasized the employee experience. Within the past year, we have won this award across three regions: the US, India, and LATAM.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

MARCH 29, 2024

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. Over the years, one of our primary focuses became Snowflake and migrating customers to this leading cloud data platform.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

FEBRUARY 2, 2024

Founded in 2014 by three leading cloud engineers, phData focuses on solving real-world data engineering, operations, and advanced analytics problems with the best cloud platforms and products. This search for efficiency led us to create the Data Source tool, which is part of the phData Toolkit.

SQL 52

SQL Database Data Quality Data Engineering 52

Alation

SEPTEMBER 28, 2022

DataOps is a set of technologies, processes, and best practices that combine a process-focused perspective on data and the automation methods of the Agile software development methodology to improve speed and quality and foster a collaborative culture of rapid, continuous improvement in the data analytics field. Source: Google Trends.

DataOps 52

DataOps Data Pipeline Data Quality Analytics 52

DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

AssemblyAI

AUGUST 15, 2023

Around 2012 to 2014, developers proposed updating these modules, but were told to use third party libraries instead. Postgres Replication Issue There is an update on work to address lag in Postgres replication for the data engineering team. However, over time these modules became outdated.

Python 52

Python Machine Learning Machine Learning Algorithm 52

ODSC - Open Data Science

JANUARY 29, 2024

Generative AI Generative AI is another crucial skill for the role of prompt engineering, as it encompasses the core ability to leverage AI to create new content, whether it be text, images, or other forms of media. GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

phData

MAY 25, 2023

Snowflake was originally launched in October 2014, but it wasn’t until 2018 that Snowflake became available on Azure. This enabled their data engineering teams to create fast and efficient data pipelines that helped feed Power BI reports and eliminated hours of manual work to update Excel and CSV files.

Power BI 84

Power BI Analytics Analytics Azure 84

AWS Machine Learning Blog

APRIL 5, 2024

Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020. Shravan Kumar is a Senior Director of Client success at Gramener, with decade of experience in Business Analytics, Data Evangelism & forging deep Client Relations.

Clustering 134

Clustering ML ML AWS 134

DrivenData Labs

DECEMBER 11, 2024

Looking back ¶ When we started DrivenData in 2014, the application of data science for social good was in its infancy. There was rapidly growing demand for data science skills at companies like Netflix and Amazon. Prominent use cases focused on marketing and content recommendations.

Data Science 147

Data Science Data Scientist Machine Learning Machine Learning 147

Tableau

DECEMBER 1, 2021

The visual encoding allowed domain experts to immediately see that blended data was inappropriate, which is why Blending was useful to people who did not understand joins. . The Data Tab was added in v8.2 June 2014) to give people who understand joins a better experience than a dialog. A key early feature was Extracts in v2.0

Tableau 145

Tableau ML ML Database 145

phData

NOVEMBER 9, 2023

Effectively this is a way to store the source of truth and build (or rebuild) your downstream data products (including data warehouses) from it. What is the Difference Between a Data Lake and a Data Warehouse? Historically, there were big differences.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Tableau

DECEMBER 1, 2021

The visual encoding allowed domain experts to immediately see that blended data was inappropriate, which is why Blending was useful to people who did not understand joins. . The Data Tab was added in v8.2 June 2014) to give people who understand joins a better experience than a dialog. A key early feature was Extracts in v2.0

Tableau 98

Tableau ML ML Database 98