Smart Data Collective

SEPTEMBER 21, 2021

Companies these days have multiple on-premise as well as cloud platforms to store their data. The data contained can be both structured and unstructured and available in a variety of formats such as files, database applications, SaaS applications, etc. Data quality and governance.

Data Quality 131

Data Quality Data Pipeline Database Internet of Things 131

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Dolt Dolt is an open-source relational database system built on Git.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Pickl AI

JULY 19, 2023

By maintaining clean and reliable data, businesses can avoid costly mistakes, enhance operational efficiency, and gain a competitive edge in their respective industries. Best Data Hygiene Tools & Software Trifacta Wrangler Pros: User-friendly interface with drag-and-drop functionality. Provides real-time data monitoring and alerts.

Data Quality 40

Data Quality Data Profiling Data Governance Data Preparation 40

Pickl AI

AUGUST 30, 2024

This is particularly important for organisations that have grown through acquisitions and need to unify disparate data systems. Enhance Performance Moving data to more efficient storage solutions can improve performance and reduce costs. Assessment Evaluate the existing data quality and structure.

Data Quality 52

Data Quality Data Governance Azure Database 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 91

phData

DECEMBER 29, 2022

This automation includes things like SQL translation during a data platform migration (SQLMorph), making changes to your Snowflake information architecture (Tram), and checking for parity and data quality between platforms (Data Source Automation). table1, match on the Snowflake database and table (ignoring the schema).

SQL 52

SQL Database Database Administration Data Profiling 52

Dataconomy

JULY 28, 2023

How to become a data scientist Data transformation also plays a crucial role in dealing with varying scales of features, enabling algorithms to treat each feature equally during analysis Noise reduction As part of data preprocessing, reducing noise is vital for enhancing data quality.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

The MLOps Blog

MARCH 15, 2023

Scalability : A data pipeline is designed to handle large volumes of data, making it possible to process and analyze data in real-time, even as the data grows. Data quality : A data pipeline can help improve the quality of data by automating the process of cleaning and transforming the data.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Alation

JANUARY 13, 2022

It’s in all types of data management systems, from databases to ERP tools, to data integration software. In fact, data intelligence technologies support building a data fabric and realizing a data mesh. Let’s turn our attention now to data mesh. What Is a Data Mesh?

Data Lakes 59

Data Lakes Data Governance Data Quality Data Warehouse 59

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

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ETL Data Pipeline ML ML 59

phData

AUGUST 17, 2023

This can significantly improve processing time and overall efficiency, enabling faster data transformation and analysis. Data Quality Management Orchestration tools can be utilized to incorporate data quality checks and validations into data pipelines. Can I use multiple orchestration tools with Snowflake?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Dataconomy

MARCH 26, 2025

Key applications of ETL pipelines ETL pipelines are utilized across various applications, making them invaluable in the world of data management. Their primary uses include: Data migration: Facilitates the transfer of data from legacy systems to modern databases, ensuring accessibility across platforms.

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