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Real-Time Data Processing
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Real-Time File Transfer
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Data Intake and Backup
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IoT Analysis
Real-Time Data Processing
DIS collects real-time vehicle traffic data and caches the data in DIS streams. The data is analyzed to facilitate traffic control.
Advantages
Real-Time
DIS receives data from application systems in real time.
High Concurrency and Low Latency
DIS supports millions of concurrent connections and millisecond-level response time.
Openness
DIS can work with various analysis platforms to provide one-stop data transmission and computing.
Real-Time File Transfer
DIS collects files from application systems in real time, uploads them to the cloud, and identifies VIP customers and potential customer needs through analysis and machine learning.
Advantages
Multiple Data Sources
DIS collects data from multiple data sources. The collection agent can be integrated into different application systems.
Easy to Use
DIS provides agents for different platforms to simplify data collection.
High Security
HTTPS-encrypted transmission ensures data security.
Data Intake and Backup
DIS backs up a lot of log files to the cloud for data recovery or exception analysis. Small log files can be merged and dumped into a single large file to accelerate data processing.
Advantages
Multiple Data Sources
DIS supports open source systems such as Kafka and Flume and therefore can easily integrate with other systems.
Efficient Data Transmission
You can scale the DIS stream capacity based on the volume of input data.
Massive Small File Transfer
Small files are migrated to the cloud and then dumped to OBS.
Related Services
obs
IoT Analysis
DIS SDKs are integrated into vehicle-mounted devices to upload vehicle monitoring data to the cloud in real time. The data is analyzed to monitor device status and detect vehicle faults in advance.
Advantages
High Concurrency and Low Latency
DIS supports millions of concurrent connections and millisecond-level response time.
Seamless Interoperability
DIS works seamlessly with CS, MRS, and other services for fast data analysis and processing.
Flexibility and Openness
DIS provides open APIs and multi-language SDKs across different platforms.
Functions
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Data Collection
DIS provides RESTful APIs, SDKs, and agents to collect offline data from various sources in real time.
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Data Transmission
DIS transmits data in real time with high concurrency, high reliability, and low latency.
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Data Movement
DIS provides multiple connectors to move data freely between storage, computing, and analysis services.
Multiple data sources
DIS collects data from user application systems, Kafka systems, and Flume systems in real time.
Multiple data intake modes
DIS provides RESTful APIs, SDKs, and agents for real-time data intake with minimum configuration required.
DIS supports millions of concurrent connections. Each DIS stream can ingest terabytes of data per day, and each partition supports data write speeds of up to hundreds of gigabytes per day.
The throughput of each DIS stream can scale dynamically by adding partitions.
DIS is integrated with the OBS, MRS, DWS, CloudTable, DLI, CS, and FunctionGraph to simplify data dumping, computing, and analysis.
Data can be imported to cloud services such as CloudTable, DWS, HBASE, RDS, and DCS by simply configuring the agents between DIS and these cloud services.
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Data Processing
DIS comes preloaded with the connectors to extract, aggregate, convert, and preprocess data input from users.
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Data Caching
DIS preserves real-time data for up to 7 days depending on configurations, ensuring data security.
DIS converts data from CSV/JSON format to Parquet/CarbonData format, improving user data quality.
