Monte Carlo

OCTOBER 31, 2024

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. AI data engineers are the first line of defense against unreliable data pipelines that serve AI models.

Data Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 52

Data Engineering Weekly

MARCH 3, 2024

Here is the agenda, 1) Data Application Lifecycle Management - Harish Kumar( Paypal) Hear from the team in PayPal on how they build the data product lifecycle management (DPLM) systems. 3) DataOPS at AstraZeneca The AstraZeneca team talks about data ops best practices internally established and what worked and what didn’t work!!!

Data Engineer 67

Data Engineer Data Engineering Engineering Pipeline-centric 67

Precisely

MARCH 14, 2023

Read Quality data you can depend on – today, tomorrow, and beyond For many years Precisely customers have ensured the accuracy of data across their organizations by leveraging our leading data solutions including Trillium Quality, Spectrum Quality, and Data360 DQ+. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders. Strong analytical and technical skills to address sophisticated issues.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Precisely

JANUARY 22, 2024

The challenge is that many businesses struggle to turn their data into usable assets for CX applications. According to Corinium, only 37% of organizations have a well-developed enterprise data architecture that enables high-quality, data-driven, and personalized CX.

Data Integration 59

Data Integration High Quality Data Machine Learning Data Governance 59

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineer 52

Data Engineer Data Engineering Engineering Metadata 52

Monte Carlo

AUGUST 31, 2023

Not long after data warehouses moved to the cloud, so too did data lakes (a place to transform and store unstructured data), giving data teams even greater flexibility when it comes to managing their data assets. As data becomes more and more foundational to business, data teams will only grow.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

The Modern Data Company

DECEMBER 29, 2022

For example, using artificial intelligence and machine learning, banks can better protect customer identities across multiple channels while ensuring that sensitive customer data remains absolutely secure. In addition, financial data is highly sensitive and subject to multiple regulations and privacy requirements.

Banking 52

Banking Pipeline-centric Building Finance 52

DataKitchen

NOVEMBER 4, 2021

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Monte Carlo

JANUARY 31, 2023

The Outcomes: Better Visibility, Faster Issue Resolution, and Greater Scalability Monte Carlo’s data reliability dashboard offers a holistic look at data platform assets and data quality at scale. Monte Carlo helped the Checkout.com data team combat those challenges.

Data 52

Data Data Engineer Data Engineering High Quality Data 52

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

Knowledge Hut

MARCH 5, 2024

These professionals use their computer science and design skills to frame data infrastructure, plan future databases, and store and manage data for organizations and users. Technology According to a Glassdoor report, data engineering average salary at large companies generally ranges from S$86,288 to S$171,980.

Data Engineer 52

Data Engineer Data Engineering Engineering Education 52

Monte Carlo

MARCH 28, 2024

Self-serve tooling is also one of the main principles of the data mesh concept—a new approach to decentralized data architecture. Do you have a centralized data team that handles every aspect of data management and application? If data is your product, then it needs to be high quality.

Data 52

Data High Quality Data Machine Learning Accessible 52

Databand.ai

JANUARY 24, 2023

The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models, and related artifacts.” Critically, teams can’t achieve these goals if they don’t have the right data that’s well governed and trusted. Therefore, DataOps is critical to powering DevOps.

Retail 52

Retail Banking Government Manufacturing 52

Monte Carlo

MAY 17, 2023

Many times this is by freeing them from having to manually implement and maintain hundreds of data tests as was the case with Contentsquare and Gitlab. “We We had too many manual data checks by operations and data analysts,” said Otávio Bastos, former global data governance lead, Contentsquare. “It

Data 52

Data Data Pipeline Data Engineer Data Engineering 52

Monte Carlo

MAY 17, 2023

Many times this is by freeing them from having to manually implement and maintain hundreds of data tests as was the case with Contentsquare and Gitlab. “We We had too many manual data checks by operations and data analysts,” said Otávio Bastos, former global data governance lead, Contentsquare. “It

Data Pipeline 52

Data Pipeline Data Engineer Data Engineering Data 52