akshay L

akshay L

1581415216

Data Warehouse Tutorial | Data Warehouse Training | What is Data Warehouse | Intellipaat

In this data warehouse tutorial you will learn what is data warehouse, need of data warehouse, types of data warehouse, various data warehouse concepts and popular data warehouse tools in details.

Why should you watch this Data Warehouse tutorial?

Major corporations are investing huge amounts of money in order to derive more value from the data that they collect. This mean Business Intelligence and Data Warehousing tools like Erwin will see a huge upside due to rapid adoption. Our Data Warehouse tutorial has been created with extensive inputs from the industry so that you can learn this easily.

Who should watch this Data Warehouse tutorial?

If you want to learn Data Warehouse to become fully proficient to work with large amounts of data then this Intellipaat explanation on Data Warehouse video is for you. This Intellipaat Data Warehouse tutorial is your first step to learn Data Warehouse. We are covering the most important Data Warehouse examples in this tutorial. Since this Data Warehouse tutorial for beginners video can be taken by anybody, so if you are a Database Administrators, Database Modelers, Analytics Managers, ETL and BI Developers, Data Scientists and Analysts or those looking for a career in Data warehousing you can watch this video.

#DataWarehousing #DataWarehouseTutorial #DataWarehouseTraining #WhatisDataWarehouse #DataWarehouse

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Data Warehouse Tutorial | Data Warehouse Training | What is Data Warehouse | Intellipaat
Sid  Schuppe

Sid Schuppe

1618404240

Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses

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

Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization

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

Siphiwe  Nair

Siphiwe Nair

1622616540

Top 30 Data Warehouse Interview Questions & Answers in 2021 [For Freshers & Experienced]

Data warehouse interview questions listed in this article will be helpful for those who are in the career of data warehouse and business intelligence. With the advent of machine learning, a large volume of data needs to be analyzed to get the insights and implement results faster. Those days are gone when the data processing steps were data storage, assimilation, fetching, and processing. But as the volume of data increases, such data needs to be processed and show instant results.

All the businesses such as healthcare, BFSI, utilities, and many government organizations are changing to the data warehouse. As a result of this, more professionals having expertise in the data warehouse get hired so that they can analyze the large volumes of data and provide relevant insights. Thus, data warehouse interview questions become pertinent to easily crack the interviews and to get important knowledge.

If you are passionate about handling massive data and managing databases, then a data warehouse is a great career option for you. In this article, you will get the data warehouse interview questions that can help you with your next interview preparation. The questions are from basic to expert level, so both fresher and experienced professionals will get benefited from these data warehouse interview questions.

#data science #data warehouse #data warehouse interview #data warehouse interview questions #data warehouse interview questions and answers