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Introduction to SQL for Data Engineering

Analytics Vidhya

Introduction In this article, we will be looking for a very common yet very important topic i.e. SQL also pronounced as Ess-cue-ell. So this time I’ll be answering some of the factual questions about SQL which every beginner needs to know before getting […].

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Navigate your way to success – Top 10 data science careers to pursue in 2023

Data Science Dojo

Data Engineer Data engineers are responsible for building, maintaining, and optimizing data infrastructures. They require strong programming skills, expertise in data processing, and knowledge of database management.

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Why SQL is important for Data Analyst?

Pickl AI

Data Analysis is one of the most crucial tasks for business organisations today. SQL or Structured Query Language has a significant role to play in conducting practical Data Analysis. That’s where SQL comes in, enabling data analysts to extract, manipulate and analyse data from multiple sources.

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Where to Find Snowflake Training Resources

phData

Snowflake’s SnowPro Advanced Certifications assess advanced Snowflake knowledge and skills relating to five data science roles. The SnowPro Advanced Administrator Certification targets Snowflake Administrators, Snowflake Data Cloud Administrators, Database Administrators, Cloud Infrastructure Administrators, and Cloud Data Administrators.

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How to Set up a CICD Pipeline for Snowflake to Automate Data Pipelines

phData

In recent years, data engineering teams working with the Snowflake Data Cloud platform have embraced the continuous integration/continuous delivery (CI/CD) software development process to develop data products and manage ETL/ELT workloads more efficiently. What Are the Benefits of CI/CD Pipeline For Snowflake?

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Career Options after BCA – Pickl.AI

Pickl AI

Database Administrator (DBA): They are responsible for managing and maintaining databases used by organizations to store and retrieve data. They ensure data security, optimize database performance, and troubleshoot any issues that arise. Why Pursue a Course in Data Science?

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How to Version Control Data in ML for Various Data Sources

The MLOps Blog

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. Given the range of tools and data types, a separate data versioning logic will be necessary.

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