Remove Data Observability Remove ETL Remove SQL
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Top ETL Tools: Unveiling the Best Solutions for Data Integration

Pickl AI

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. Data Profiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape.

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Maximize the Power of dbt and Snowflake to Achieve Efficient and Scalable Data Vault Solutions

phData

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. The most important reason for using DBT in Data Vault 2.0 Managing a data vault with SQL is a real challenge.

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Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. Aggregation: Summarising data into meaningful metrics or aggregates.

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How to Combat the Lack of Standardization in Snowflake

phData

Thankfully there are open-source projects that don’t make you parse SQL into grammars yourself (ain’t nobody got time for that!), SQL Linting saves tons of time and ensures your team is looking for deeper logical issues in the PR instead of basic naming and formatting mistakes. such as SQLFluff.

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

To power AI and analytics workloads across your transactional and purpose-built databases, you must ensure they can seamlessly integrate with an open data lakehouse architecture without duplication or additional extract, transform, load (ETL) processes.

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Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. Like they didn’t have to think about, you know, data observability, but look, if you provided those data, we captured things about it.

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