Zara  Bryant

Zara Bryant

1611885313

A Tour of SQL Workshops | Data Exposed

Join Anna Hoffman and Buck Woody as they take a tour of the incredible learning resource the Microsoft Engineering team has put together. From labs to full hands-on workshops and much more, you’ll find a wealth of high-quality, free-cost training. You’ll also learn about many of the other valuable references and assets you can find at SQL Workshops. To get started with SQL Workshops, click: https://aka.ms/sqlworkshops?WT.mc_id=dataexposed-c9-niner

  • 0:00 Introduction
  • 1:05 What is SQL Workshops
  • 2:41 Walking through SQL Workshops

#sql #database #developer

What is GEEK

Buddha Community

A Tour of SQL Workshops  | Data Exposed
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Sasha  Lee

Sasha Lee

1624589676

SQL for Data Science

Currently, the demand for the skill of SQL is on the rise. Most of the jobs describe their skill requirements, and while doing that, they mention the knowledge in SQL specifically. As the name suggests, ‘Data Science’ is data-driven. Thus, SQL will be an integral part of any data science job. It is also because of the advantage that it offers among the other alternatives. This article tries to elaborate on why SQL and querying are essential for data science and related roles.

If you want to learn SQL for data science, then you can start your journey here!

Structured Query Language, acronymized to SQL, is a computer programming language aimed and designed to manipulate data warehoused in RDBMSs, i.e., Relational Database Management Systems. Different functions such as insertion, deletion, updating, modification of data can be done using SQL. Since most of the structured data is stored in RDBMSs, working with data science will necessarily involve RDBMS and, hence, SQL.

With the advent of big data, data warehousing using relational database management systems has gained more importance, and it is strictly necessary to use them. Moreover, traditionally along with the programming languages Python and R, SQL is used. For instance, a data scientist can write an SQL query to extract data from a database, on which further analyses can be made using Python or R.

If you want to become a data scientist, then you can start your journey here!

Why SQL for Data Science?

Data Science is simply the analysis and study of data to extract meaningful insights. SQL comes into the picture in two of the most critical steps of a data science cycle — Data Extraction, the pre-processing step, as mentioned in the introduction, and Machine Learning. Most of the database platforms are designed using SQL, as it has become a standard for database systems. Also, it is easy to communicate with databases with complex instructions and manipulate data.

Modern systems such as Hadoop, Spark use SQL to maintain relational database systems and to process structured data. Identification of suitable data sources and pre-processing are the key steps in any data analysis work. Since the data is stored in relational databases, querying to extract the data without copying the entire database is necessary as it saves time and is efficient. Hence, a data scientist needs to have comprehensive knowledge in querying language, SQL.

Importance of SQL

SQL is a comprehensive language with several functions, statements, and operators that pave the way to seamless data extraction. SQL has multiple reasons to assert its importance and relevance in data science. First of all, even though SQL has a wide range of tools available, learning them is not an arduous task, as the commands and queries in SQL are comparable to simple English. For example, consider the SQL query ‘select name, nationality from employee’, which can be comprehended by any person of its function with its simplicity of language. Thus, a data science novice can quickly learn SQL, unlike the other programming languages that require more conceptual understanding.

#data-analysis #data #data-science #sql #data-visualization #sql

Siphiwe  Nair

Siphiwe Nair

1625133780

SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

**Your expertise needed! **Fill up our quick Survey

#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database