Ruth  Nabimanya

Ruth Nabimanya

1625027100

What Is MongoDB? Introduction, Data Modeling, Terminology & Hierarchy

MongoDB is an open-source, document-oriented, leading NoSQL database for huge data storage volumes. It is completely coded in one of the primitive but relatively very advanced and trustworthy languages, C++. Instead of using traditional relational databases defined on the basic concept of tables and rows, MongoDB relies on the use of documents and collections containing the key-value pairs and making up the platform’s basics.

It works on multiple platforms with the ability to interact with each other. Some of the notable features of MongoDB include high availability time, best performance, and easy scalability.

Now, to better understand what MongoDB is, we should know all the terminology along with the structure in which MongoDB stores the database.

Database

Collection

Document

#big data #mongodb #what is mongodb #data modeling #terminology #hierarchy

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What Is MongoDB? Introduction, Data Modeling, Terminology & Hierarchy
Ruth  Nabimanya

Ruth Nabimanya

1625027100

What Is MongoDB? Introduction, Data Modeling, Terminology & Hierarchy

MongoDB is an open-source, document-oriented, leading NoSQL database for huge data storage volumes. It is completely coded in one of the primitive but relatively very advanced and trustworthy languages, C++. Instead of using traditional relational databases defined on the basic concept of tables and rows, MongoDB relies on the use of documents and collections containing the key-value pairs and making up the platform’s basics.

It works on multiple platforms with the ability to interact with each other. Some of the notable features of MongoDB include high availability time, best performance, and easy scalability.

Now, to better understand what MongoDB is, we should know all the terminology along with the structure in which MongoDB stores the database.

Database

Collection

Document

#big data #mongodb #what is mongodb #data modeling #terminology #hierarchy

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

Enterprise Data Management: Stick to the Basics

Lots of people have increasing volumes of data and are trying to run data management programs to better sort it. Interestingly, people’s problems are pretty much the same throughout different sectors of any industry, and data management helps them configure solutions.

The fundamentals of enterprise data management (EDM), which one uses to tackle these kinds of initiatives, are the same whether one is in the health sector, a telco travel company, or a government agency, and more! Therefore, the fundamental practices that one needs to follow to manage data are similar from one industry to another.

For example, suppose you’re about to set off and design a program. In this case, it may be your integration platform project or your big warehouse project; however, the principles for designing that program of work is pretty much the same regardless of the actual details of the project.

#big data #bigdata #big data analytics #data management #data modeling #data governance #enterprise data #enterprise data management #edm

Cyrus  Kreiger

Cyrus Kreiger

1617731760

An Introduction to Data Connectors: Your First Step to Data Analytics

Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.

Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:

  • MySQL database that details all the interactions your users are having with your product
  • Salesforce account that contains the latest information about your current and prospective customers
  • Zendesk account that has all support tickets raised by your customers

#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning