Data Science Dojo

FEBRUARY 20, 2023

Best tools and platforms for MLOPs – Data Science Dojo Google Cloud Platform Google Cloud Platform is a comprehensive offering of cloud computing services. It offers a range of products, including Google Cloud Storage, Google Cloud Deployment Manager, Google Cloud Functions, and others.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The Data Engineer Not everyone working on a data science project is a data scientist. Data engineers are the glue that binds the products of data scientists into a coherent and robust data pipeline.

Data Science 130

Data Science Data Scientist ML ML 130

ODSC - Open Data Science

FEBRUARY 17, 2023

Computer science, math, statistics, programming, and software development are all skills required in NLP projects. Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops.

Data Science 111

Data Science Deep Learning Deep Learning Natural Language Processing 111

Precisely

APRIL 11, 2024

As a Technical Architect at Precisely, I’ve had the unique opportunity to lead the AWS Mainframe Modernization Data Replication for IBM i initiative, a project that not only challenged our technical capabilities but also enriched our understanding of cloud integration complexities.

AWS 72

AWS Cloud Computing Database Data Pipeline 72

Analytics Vidhya

FEBRUARY 17, 2023

Introduction Are you curious about the latest advancements in the data tech industry? Perhaps you’re hoping to advance your career or transition into this field. In that case, we invite you to check out DataHour, a series of webinars led by experts in the field.

Analytics 319

Analytics Analytics Data Pipeline Data Warehouse 319

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

Azure 283

Azure ETL Analytics Analytics 283

Dataversity

JANUARY 25, 2023

Over the past few years, enterprise data architectures have evolved significantly to accommodate the changing data requirements of modern businesses. Data warehouses were first introduced in the […] The post Are Data Warehouses Still Relevant?

Data Warehouse 52

Data Warehouse Data Lakes Cloud Computing Data Pipeline 52

Precisely

JANUARY 19, 2023

Yet mainframes weren’t designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

phData

FEBRUARY 21, 2025

In this post, we will be particularly interested in the impact that cloud computing left on the modern data warehouse. We will explore the different options for data warehousing and how you can leverage this information to make the right decisions for your organization.

Data Warehouse 52

Data Warehouse Cloud Data ETL Cloud Computing 52

Smart Data Collective

AUGUST 17, 2022

But keep in mind one thing which is you have to either replicate the topics in your cloud cluster or you will have to develop a custom connector to read and copy back and forth from the cloud to the application. 5 Key Comparisons in Different Apache Kafka Architectures.

Apache Kafka 130

Apache Kafka ETL Data Lakes AWS 130

Data Science Dojo

JULY 3, 2024

Data Engineering : Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing. Cloud Computing : Utilizing cloud services for data storage and processing, often covering platforms such as AWS, Azure, and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

AUGUST 6, 2024

Serverless, or serverless computing, is an approach to software development that empowers developers to build and run application code without having to worry about maintenance tasks like installing software updates, security, monitoring and more. Despite its name, a serverless framework doesn’t mean computing without servers.

Cloud Computing 45

Cloud Computing Internet of Things Big Data Big Data 45

The MLOps Blog

JUNE 27, 2023

Monte Carlo Monte Carlo is a popular data observability platform that provides real-time monitoring and alerting for data quality issues. It could help you detect and prevent data pipeline failures, data drift, and anomalies. Metaplane supports collaboration, anomaly detection, and data quality rule management.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

JUNE 19, 2023

Scalable and flexible infrastructure  — Processing big data requires an infrastructure that adapts to rapidly growing processing needs and different scenarios of data storage and usage. This entails the use of other technologies such as distributed computing, edge computing, and cloud computing.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Smart Data Collective

JUNE 20, 2022

Failing to make production data accessible in the cloud. Data professionals often enable many different cloud-native services to help users perform distributed computations, build and store container images, create data pipelines, and more.

Data Engineering 145

Data Engineering Data Engineering Data Engineer Data Engineering 145

Precisely

APRIL 6, 2023

That creates new challenges in data management and analytics. Each new system comes with its own schema, which must be mapped and normalized alongside other data. The best integration tools make it easy to build and deploy data pipelines to accommodate the ever-changing needs of modern financial services organizations.

Data Quality 52

Data Quality Data Pipeline Analytics Analytics 52

Precisely

JANUARY 18, 2024

Yet mainframes weren’t initially designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Artificial Intelligence Artificial Intelligence Cloud Computing 52

Alation

JANUARY 17, 2023

These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

phData

OCTOBER 12, 2023

When the data or pipeline configuration needs to be changed, tools like Fivetran and dbt reduce the time required to make the change, and increase the confidence your team can have around the change. These allow you to scale your pipelines quickly. Governance When talking about scaling, governance doesn’t often come up.

Data Warehouse 52

Data Warehouse Database Cloud Data Data Pipeline 52

Pickl AI

JULY 8, 2024

Security is Paramount Implement robust security measures to protect sensitive time series data. Integration with Data Pipelines and Analytics TSDBs often work in tandem with other data tools to create a comprehensive data ecosystem for analysis and insights generation.

Database 52

Database Data Pipeline Machine Learning Machine Learning 52

Alation

APRIL 14, 2022

A cloud-ready data discovery process can ease your transition to cloud computing and streamline processes upon arrival. So how do you take full advantage of the cloud? Migration leaders would be wise to enable all the enhancements a cloud environment offers, including: Special requirements for AI/ML.

Data Governance 59

Data Governance ML ML Cloud Data 59

phData

MAY 25, 2023

Snowflake is a cloud computing–based data cloud company that provides data warehousing services that are far more scalable and flexible than traditional data warehousing products. Table of Contents Why Discuss Snowflake & Power BI?

Power BI 84

Power BI Analytics Analytics Azure 84

phData

JANUARY 5, 2024

Understanding the Cost of Snowflake Like any other cloud computing tool, costs can quickly add up if not kept in check. The total cost of using Snowflake is the aggregate of the cost of using data transfer, storage, and computing resources.

Power BI 52

Power BI Cloud Computing Data Pipeline AWS 52

The MLOps Blog

DECEMBER 7, 2022

The inherent cost of cloud computing : To illustrate the point, Argentina’s minimum wage is currently around 200 dollars per month. The CI/CD was crucial for preventing accidents such as unwanted pipeline executions, and we implemented the use of GitHub Actions to trigger some tasks , such as the data pipeline deployment.

ML 52

ML ML AWS ETL 52

phData

JANUARY 15, 2024

PCI-DSS (Payment Card Industry Data Security Standard): Ensuring your credit card information is securely managed. HITRUST: Meeting stringent standards for safeguarding healthcare data. CSA STAR Level 1 (Cloud Security Alliance): Following best practices for security assurance in cloud computing.

Data Governance 52

Data Governance Database Data Warehouse Cloud Computing 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Pickl AI

SEPTEMBER 5, 2024

By leveraging Azure’s capabilities, you can gain the skills and experience needed to excel in this dynamic field and contribute to cutting-edge data solutions. Microsoft Azure, often referred to as Azure, is a robust cloud computing platform developed by Microsoft. What is Azure?

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Dataconomy

FEBRUARY 12, 2024

Businesses increasingly rely on up-to-the-moment information to respond swiftly to market shifts and consumer behaviors Unstructured data challenges : The surge in unstructured data—videos, images, social media interactions—poses a significant challenge to traditional ETL tools.

ETL 195

ETL Data Governance Machine Learning Machine Learning 195

Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

Data Analyst 52

Data Analyst Data Scientist Data Science Machine Learning 52

Precisely

FEBRUARY 10, 2023

According to the IDC report, “organizations that have implemented DataOps have seen a 40% reduction in the number of data and application exceptions or errors and a 49% improvement in the ability to deliver data projects on time.”

DataOps 52

DataOps Data Observability ML ML 52

AWS Machine Learning Blog

DECEMBER 18, 2024

All data generation and processing steps were run in parallel directly on the SageMaker HyperPod cluster nodes, using a unique working environment and highlighting the clusters versatility for various tasks beyond just training models. In his free time, Giuseppe enjoys playing football.

Clustering 98

Clustering AWS AI AI 98

IBM Journey to AI blog

FEBRUARY 9, 2024

But the most impactful developments may be those focused on governance, middleware, training techniques and data pipelines that make generative AI more trustworthy , sustainable and accessible, for enterprises and end users alike. Here are some important current AI trends to look out for in the coming year.

AI 119

AI AI Artificial Intelligence Artificial Intelligence 119