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What are the best techniques for modeling time-series data in Teradata?

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Time-series data is a type of data that records values or events over time, such as sales, temperatures, or sensor readings. Modeling time-series data in Teradata, a relational database management system, can be challenging due to the high volume, velocity, and variety of the data. However, there are some best techniques that can help you design, store, and query time-series data in Teradata effectively and efficiently. In this article, we will discuss four of these techniques and how they can benefit your database development.

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REJUSTE WENOUMI

Economics, Database Developer | Business Process Automation Specialist | Certified data and AI developer, and SAP ERP |…

1 Use temporal tables

Temporal tables are a feature of Teradata that allows you to store and manage time-related information in your tables. Temporal tables have two types of columns: valid-time and transaction-time. Valid-time columns capture the period of time when a row is true or valid in the real world, such as the start and end dates of a contract. Transaction-time columns capture the period of time when a row is current or active in the database, such as the timestamp of an insert or update. Temporal tables enable you to perform time-based queries and analytics on your data, such as finding the historical, current, or future state of a row, or comparing the changes over time.

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Md. Zahid Hossain

Software Engineer(OCP), International Speaker, Author at Amazon Author Central, Fellow at DEA and ECOTA, Ambassador at YEP Forum(Energy UK), ISLD(USA), CENTA(India) and MM360(USA), Member at ASM, Reviewer at IJRISS
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Modeling time-series data effectively in Teradata requires a combination of powerful features and strategic best practices. Here are some top techniques to consider: Utilizing Temporal Tables Leveraging Time-series Functions Exploiting Partitioned Primary Indexes Implementing Compression Techniques Modeling Approaches: Explore ARIMA (Autoregressive Integrated Moving Average) Consider Holt-Winters Evaluate machine learning models Additional Tips: Data preprocessing Performance monitoring Error analysis Stay updated

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Zohaib Akhter

Software Engineer | MERN Stack developer | Nest.js | Node GRPC | Java | Spring Boot | Microservices | MongoDB | MySQL | AWS | Hostinger | Docker
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In Teradata, leveraging temporal tables is a powerful technique for modeling time-series data. By incorporating temporal features, such as system-versioning, users can efficiently track changes to data over time. This approach simplifies the querying and analysis of historical data, providing a seamless means to capture and analyze temporal variations within the Teradata environment. Integrating temporal tables enhances data modeling capabilities, making it easier to manage and derive insights from time-series datasets.

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2 Use time-series functions

Time-series functions are a set of functions that allow you to perform complex calculations and transformations on time-series data in Teradata. Time-series functions can help you aggregate, interpolate, normalize, align, or fill missing values in your data. For example, you can use the TIMESERIES clause to generate a regular series of time intervals from your data, such as hourly, daily, or monthly. You can also use the EXPAND ON clause to fill the gaps in your data with interpolated or extrapolated values, such as linear, spline, or constant. Time-series functions can help you prepare your data for further analysis or reporting.

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3 Use partitioned primary indexes

Partitioned primary indexes (PPIs) are a way of organizing your data into logical partitions based on one or more columns in your table. PPIs can help you improve the performance and scalability of your queries on time-series data in Teradata. PPIs can help you reduce the amount of data that needs to be scanned, sorted, or redistributed by the database. PPIs can also help you avoid skewing, which is the uneven distribution of data across the nodes of the system. For time-series data, you can use PPIs to partition your data by date or time intervals, such as year, month, week, day, or hour. This can help you access only the relevant partitions for your queries, such as filtering by a specific date range or aggregating by a specific time period.

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4 Use compression techniques

Compression techniques are a way of reducing the size of your data and saving disk space in Teradata. Compression techniques can help you optimize the storage and retrieval of your time-series data in Teradata. Compression techniques can help you improve the performance and efficiency of your queries by reducing the I/O operations and network traffic. Compression techniques can also help you lower the cost and maintenance of your system. For time-series data, you can use compression techniques such as algorithmic compression (ALC), block-level compression (BLC), or columnar compression. These techniques can help you compress your data based on patterns, blocks, or columns, respectively. These techniques can be especially useful for time-series data that has high redundancy, low variability, or high cardinality.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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REJUSTE WENOUMI

Economics, Database Developer | Business Process Automation Specialist | Certified data and AI developer, and SAP ERP | Management Information Systems (MIS) Professional
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Pour modéliser des données de séries chronologiques dans Teradata, différentes méthodes peuvent être utilisées, notamment les moyennes mobiles, le lissage exponentiel, les modèles ARIMA, la décomposition saisonnière, la régression sur les séries chronologiques et les réseaux LSTM. Chacune de ces méthodes présente des avantages et des limitations, et le choix de la méthode dépend des caractéristiques spécifiques des données et des objectifs de l'analyse. Teradata offre également diverses fonctions et outils analytiques pour faciliter l'analyse des séries chronologiques, tels que Teradata Aster Analytics et les fonctions d'analyse avancée de Teradata Vantage.

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What are the best techniques for modeling time-series data in Teradata?

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5

1 Use temporal tables

2 Use time-series functions

3 Use partitioned primary indexes

4 Use compression techniques

5 Here’s what else to consider

Database Development

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What are the best techniques for modeling time-series data in Teradata?

1

2

3

4

5

1 Use temporal tables

2 Use time-series functions

3 Use partitioned primary indexes

4 Use compression techniques

5 Here’s what else to consider

Database Development

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