Skip to main content

Data Collection

Timeplus supports multiple ways to load data into the system, or access the external data without copying them in Timeplus:

Add new sources via web console​

Load streaming data from Apache Kafka​

As of today, Kafka is the primary data integration for Timeplus. With our strong partnership with Confluent, you can load your real-time data from Confluent Cloud, Confluent Platform, or Apache Kafka into the Timeplus streaming engine. You can also create external streams to analyze data in Confluent/Kafka/Redpanda without moving data.

Learn more.

Load streaming data from Apache Pulsar​

Apache® Pulsar™ is a cloud-native, distributed, open source messaging and streaming platform for real-time workloads. Timeplus added the integration for Apache Pulsar as both a data source and a data sink.

Learn more.

Load streaming data from Kinesis​

If your streaming data resides in Amazon Kinesis Data Stream, you can load them into Timeplus in two steps.

  1. First load the Kinesis data into Kafka topics via Amazon Kinesis Source Connector for Confluent Cloud or Amazon Kinesis Source Connector for Confluent Platform
  2. Use the above Kafka source in Timeplus to load data into streams.

The data flow can be illustrated as the following:

Upload local files​

If you have some static dataset or lookup tables in the CSV format, you can upload the files directly to Timeplus.

  1. Click the Add Data from the navigation menu. Then click Import from CSV button
  2. Drag and drop a CSV file from your local file system to upload the file. (COMING SOON: if your file is downloadable from a URL or S3 bucket, you can create a source to have Timeplus server to load it. File formats other than CSV will be supported too.)
  3. Choose whether the first row of the file is the column header.
  4. Specify a name for the stream, and optionally provide a readable description.
  5. Click the button to start uploading data and click the View Imported Data button to run a query to review imported data.

Load sample streaming data​

If you are not ready to load your real data into Timeplus, or just want to play with the system, you can use this feature to load some sampling streaming data. We provide 3 typical streaming data.

  1. iot will generate data for 3 devices(device_0, device_1 and device_2). The number value can be anything between 0 to 100. The time column is when the event is generated.
  2. user_logins will generate data for 2 users(user1 and user2), from 2 possible city values: Shanghai or Beijing. The time column is when the event is generated.
  3. devops will generate data for 3 hostname(host_0,host_1, and host_2), from 3 possible region(eu-central-1, us-west-1, and sa-east-1), 3 possible rack(1,2,3), a number usage_user from 0 to 100, usage_system from 0 to 100, and time column for the event time.

You can load such sample data via the Add Data button and the Sample Dataset option. You can create new streams or choose existing streams for the data.

Push data to Timeplus via REST or SDK​

Timeplus provides ingestion REST API, and related SDKs in different programming languages. Developers can leverage those REST API or SDK to push real-time data to Timeplus.

Learn more.

Load other data into Timeplus via 3rd party tools​

Timeplus works with the data ecosystems and can leverage various tools to load data or even do data transformation at ingestion time.

DataPM (for files and databases)​

Data Package Manager (datapm) is an open source data publishing platform for private and public use. The datapm command line tool makes moving data between systems seamless and easily repeatable. A special sink for Timeplus is shipped with the datapm command line tool out-of-box.

Learn more.

Airbyte​

AirByte provides both OSS version and managed cloud to collect data, transform data and send to other destinations.

At the high level

  1. AirByte can grab data from many different data sources, including database/CDC, or infrastructure log, application logs, or even business apps(such as Salesforce)
  2. The data can be normalized via AirByte built-in capabilities. Or it can be saved to the destination database first, then relies on dbt or other tools to apply transformations/materialization.
  3. Data collected by AirByte can be sent to many destinations, including Timeplus.

Just name a few data sources from Airbyte:

  • App marketplace such as Apple App Store
  • AWS Cloudtrail
  • Google BigQuery
  • Load file from S3/GCS/SFTP/local with Gzip/Zip/xz/Snappy compression, in CSV/JSON/XML/Excel/Parquet/etc
  • Github, GitLab, or JIRA activities
  • Google Ads
  • Instagram social media
  • Slack or Microsoft Teams
  • PostgreSQL, RedShift, Snowflake, MongoDB, MySQL, Microsoft SQL Server, etc
info

The Timeplus destination plugin for Airbyte is in the early stage. Please contact us to arrange the integration.

Kafka Connectors​

You can use Kafka Connectors to load data from popular data sources into Confluent Cloud, Confluent Platform, or Apache Kafka, then use Timeplus to load them into streams via the built-in Kafka Source.

There are a few examples of data sources that can be ingested into Timeplus via Kafka Connectors. Please check https://www.confluent.io/product/confluent-connectors/ for more details.

  • Apache ActiveMQ
  • Amazon CloudWatch Logs
  • Amazon Kinesis
  • Amazon S3
  • Amazon SQS
  • Azure Blob Storage
  • Azure Event Hubs
  • CockroachDB CDC
  • Databricks
  • Github
  • Google Cloud Pub/Sub
  • IBM MQ
  • InfluxDB
  • JDBC
  • Microsoft SQL Server
  • MongoDB
  • MQTT
  • MySQL CDC
  • Neo4j
  • Oracle Database
  • PostgreSQL CDC
  • RabbitMQ
  • Salesforce
  • ServiceNow
  • SFTP
  • SNMP
  • Splunk
  • TiDB CDC
  • Tigergraph
  • Zendesk
Last updated on by Jove Zhong