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Time Series Data

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In the modern data-focused world, time series data is all around us. This data type consists of points arranged chronologically and is identified by patterns like trends, seasonal changes, and irregularities. Time series data has unique features that make it an indispensable tool for businesses across various industries.

What is Time Series Data?

Time-series data is a sequence of data points organized chronologically, revealing how variables evolve over time. Its dynamic nature allows for tracking changes, predicting future trends and making informed decisions. Analyzing time-series data requires considerations like high data ingestion, real-time querying, scalability, and data aging.

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What is time series data
How to store time series data

How to Store Time Series Data?

Time series data, organized chronologically, allows tracking changes, predicting trends, and making informed decisions. Efficient storage is key, with factors such as data's volume, speed, query requirements, and scalability influencing the selection of storage solutions. The four main storage options include Time-Series Databases, NoSQL Databases, Relational Databases, and Data Warehouses, each with distinct strengths and limitations.


Time Series Data is Everywhere

In this tutorial video, we delve into the nature of time series data, including its unique attributes. Through examining the order of time series data, we uncover patterns such as trends and anomalies. Discover how CrateDB facilitates the execution of real-time complex queries, a vital aspect of time series data analysis.

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Time Series Data in CrateDB

In this tutorial video, we will delve into the process of querying time series data using CrateDB, demonstrating its ability to provide swift results even on extensive data sets. We will highlight CrateDB's core time-series functions, its proficiency in managing a variety of data types, and its superior scalability due to its distributed architecture. Furthermore, we will show how CrateDB can enhance data analysis and visualization, leading to more informed decision-making.

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Real-Life Challenges with Time Series

This tutorial video explores the real-world issues of time series data, such as volume, velocity, variety, and veracity. The video also discusses how CrateDB is capable of effectively tackling these challenges through appropriate data architecture.

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Whitepaper: "Guide for Time Series Projects: Data Modeling, Storage, and Lifecycle with CrateDB"

Guide for Time Series Data Projects

Gain insights into what is time series data and how it plays a critical role across various industries, from manufacturing to security. Discover how its unique complexity aids data-driven decisions and learn about its use cases beyond querying and visualization, such as anomaly detection and forecasting.

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