In a graph database, such as Neo4j, data are represented as graphs or networks of nodes (households, members, etc.) and edges or relationships between these nodes. No need to define their format beforehand, the schema will develop as you add new data.
When the real world described by the data looks like a network of concepts connected by different types of relationships.
When you need to integrate your data with data from other sources and with other formats (other surveys, climate data, remote sensing, etc.).
#database #graph database #data
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.
#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
At the very beginning of most development endeavors lies an important question: What database do I choose? There is such an abundance of database technologies at this moment, it’s no wonder many developers don’t have the time or energy to research new ones. If you are one of those developers and you aren’t very familiar with graph databases in general, you’ve come to the right place!
In this article, you will learn about the main differences between a graph database and a relational database, what kind of use-cases are best suited for each database type, and what are their strengths and weaknesses.
#graph-database #relational-database #graph-theory #graph-analysis #data-analytics #networks #data #database
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
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
Reading about the death of the relational database seems like a regular occurrence. However, here we are in 2021, and the relational data store is going strong. If we look at the DB-Engines Ranking website, six of the top 10, including the top four spots, are all relational data stores. Evidently, structured, or relational, data storage is here to stay. Yet four of the top spots are held by non-relational engines. Could that mean that relational data storage is really dying?
The core of this question is not really which kind of data store is better between a relational, normalized structure or a non-relational, denormalized storage mechanism. No, the real core question is: What kind of data store should your organization be using in 2021?
The shortest possible way I can answer this question is as follows:
All of them.
The fact is, you’re much better off not trying to answer your data needs with one, single methodology. Let’s discuss why.
#nosql #sql #data storage #relational database #data consistency #relational data #structured data #non-relational database #data stores #database choice