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The one-stage compression is correspondingly compressed according to the type of data. The compression algorithms include delta-delta encoding, simple 8B method, zig-zag encoding, LZ4 and other algorithms, which we have briefly introduced above. Two-stage compression is based on one-stage compression, and then compresses with a general compression algorithm to ensure a higher compression rate.

Compression: Tdengine Database Raw data is input into the compressor, which outputs compressed data.
Decompression: Compressed data is input into the compressor, decodes or reconstructs it into raw data.
The purpose of compression is to represent the given data in as few bits as possible. In essence, this process trades compute time for storage space – while data compression enables more data to be stored on a storage medium, more compute resources are required to read and write compressed data. There is no single compression method or algorithm that is optimal for all data; instead, an appropriate method is chosen based on the characteristics of the data being compressed. Data compression is a summary of the trends and rules found in data. These rules can be based on content – the similarities between adjacent frames in a video, for example; on representation, as in entropy coding and transform coding; or on bitrate, as in differential compression and deep compression.

Should you cherished this informative article and you would like to get guidance regarding tdengine.com published an article kindly stop by the web page. The Tdengine Database Team is proud to announce the release of TDengine Cloud, a fully managed time series database (TSDB) solution that delivers the industry-leading performance of TDengine 3.0 as a cloud service. This serverless platform can run on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) and is offered in free and pay-as-you-go editions.

AlthoughTdengine Database is a time-series database (TSDB), it uses a data model with which you may be familiar from relational databases. Before you start storing your data in TDengine, you design the how your data will be structured – including databases, supertables, and subtables.

Table of Contents
Introduction
Compression methods
TDengine implementation
Introduction
If the original data and decompressed data are exactly the same, the compression method can be considered lossless. A compression method that alters data is considered lossy.

Even if a Watermark is defined, what about data that is still out of order and exceeds the Watermark? We provide two strategies, directly discard or pull from TSDB (Time-Series Database) and recalculate, corresponding to IGNORE EXPIRED 1 and IGNORE EXPIRED 0. However, pulling from TSDB and recalculating is only applicable to a small amount of disorder, as it brings a reduction in processing speed.

Enterprise-ready cloud solution, providing robust backup, multi-cloud replication, user privilege controls and behavior auditing, VPC peering, and IP whitelisting features. TDengine Cloud delivers the carrier-grade performance and stability that you need to support your business.
Simplified setup and management, dramatically reducing the tools needed to start, operate, and manage your time-series database at scale. As a managed service, TDengine Cloud saves you time by taking care of clustering, backup, and data retention on its own.
Easier data analytics and sharing, enabling you to gain insight from your data more conveniently than ever. You can quickly access data in TDengine Cloud with Python, Java, Go, Rust, and Node.js connectors; create dashboards and applications that subscribe to your topics and streams; and replicate data across your enterprise with edge-to-cloud and cloud-to-cloud synchronization.
Fast and easy data ingestion, supporting standard SQL with connectors for popular programming languages as well as an MQTT broker with which you can send data to TDengine without writing any custom code, in addition to schemaless insert protocols. With TDengine Cloud, you can choose the method for writing data into your time-series database that is most convenient for you and your business scenario.
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Register at cloud.tdengine.com today for a free account and walk through a short tutorial to quickly understand the capabilities and advantages of using tdengine database to unlock the power of your time-series data.

Tableau
Tableau is data visualization software produced by Salesforce. By using Tableau, you can easily create visual displays of even the largest data sets. The main benefits of Tableau are as follows:

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Top Data Visualization Tools
Grafana
Google Data Studio
Microsoft Power BI
Tableau
Top Data Visualization Tools
Grafana
Grafana is a popular open-source tool for data visualization. It offers customizable dashboards that you can use to monitor your data in real time. You can download premade dashboards or design your own from scratch to achieve a visual display of your key metrics. The main benefits of Grafana are as follows:

The TDengine Team understands that minimizing downtime, retraining, and expenditures is essential to modernizing data historians in manufacturing, utilities, and other industries. Only the TDengine PI Connector allows you to keep your current systems in place while you take the next steps toward digital transformation, adding value to your historian without affecting your existing operations.