Amazon Timestream is serverless and scales up or down automatically to adjust capacity and performance, so you can concentrate on creating your applications without having to worry about managing the underlying infrastructure.
For IoT and operational applications, Amazon Timestream is a quick, scalable, and serverless time series database service that makes it simple to store and analyse trillions of events per day up to 1,000 times more quickly and for as little as 1/10th the price of relational databases. By storing recent data in memory and transferring historical data to a storage tier that is more cost-effective depending on user-defined parameters, Amazon Timestream helps you manage the lifecycle of time series data more efficiently. You can access and analyse recent and historical data together without identifying its location thanks to Amazon Timestream's adaptive query engine. With built-in time series analytics capabilities, Amazon Timestream enables you to quickly spot patterns and trends in your data. Amazon Timestream is serverless and automatically scales up or down to adjust capacity and performance, so you don’t need to manage the underlying infrastructure, freeing you to focus on building your applications.
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Ques. 1): What performance can I expect from Amazon Timestream?
Answer:
Amazon Timestream offers near real-time latencies for data ingestion. Amazon Timestream’s built-in memory store is optimized for rapid point-in-time queries, and the magnetic store is optimized to support fast analytical queries. With Amazon Timestream, you can run queries that analyze tens of gigabytes of time-series data from the memory store within milliseconds, and analytical queries that analyze terabytes of time-series data from the magnetic store within seconds. Scheduled queries further improve query performance by calculating and storing the aggregates, rollups, and other real-time analytics used to power frequently accessed operational dashboards, business reports, applications, and device-monitoring systems.
As your applications continue to send more data, Amazon Timestream automatically scales to accommodate their data ingestion and query needs. You can store exabytes of data in a single table. As your data grows over time, Amazon Timestream uses its distributed architecture and massive amounts of parallelism to process larger and larger volumes of data while keeping query latencies almost unchanged.
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Ques. 2): Before transmitting data to Amazon Timestream, do I need to build a schema?
Answer:
No. A set of dimensional properties and measures are used by Amazon Timestream to dynamically generate a table's schema. This provides a flexible and gradual description of the schema that may be changed whenever necessary without affecting availability.
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Ques. 3): How is data stored by Amazon Timestream?
Answer:
A timestamp is used by Amazon Timestream to organise and store time-series data, and dimensional attributes are used to arrange data over time. For further information, visit Werner Vogel's blog. By simply defining data retention policies to automatically migrate data from the memory store to the magnetic storage as it approaches the configured age, you may use Amazon Timestream to automate data lifecycle management.
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Ques. 4): Can I use Amazon Timestream to automatically roll up, combine, or preprocess my data?
Answer:
For calculating and storing aggregates, rollups, and other real-time analytics required to power frequently viewed operational dashboards, business reports, applications, and device monitoring systems, Amazon Timestream's scheduled queries provide a fully managed, serverless, and scalable solution.
To calculate aggregates, rollups, and other real-time analytics on your incoming data, you just describe the real-time analytics queries with scheduled queries. Amazon Timestream then periodically and automatically runs these queries and reliably stores the query results into a different table. Your dashboards, reports, programmes, and monitoring tools can then be configured to just query the destination tables rather than the much larger source tables that hold the incoming time-series data. This leads to performance and cost reductions by an order of magnitude because the destination tables contain much less data than the source tables, thereby offering faster and cheaper data access and storage.
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Ques. 5): Can I utilise an Amazon Virtual Private Cloud (VPC) with Amazon Timestream?
Answer:
Using VPC endpoints, you can connect to Amazon Timestream from your Amazon VPC. Without a network address translation (NAT) instance or an internet gateway, Amazon VPC endpoints offer dependable connectivity to Amazon Timestream APIs.
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Ques. 6): What are data from time series?
Answer:
A time series is a collection of data points that are collected over a period of time to track changing events. Examples include changes in stock prices over time, temperature readings over time, and an EC2 instance's CPU usage over time. Each data point in a time-series consists of an event that changes over time, one or more attributes, and a timestamp. This data is utilised to spot abnormalities, find chances for improvement, and gain insights into the functionality and health of an application. DevOps engineers, for instance, might be interested in data that tracks changes in infrastructure performance metrics, manufacturers might be interested in IoT sensor data that tracks equipment changes throughout a facility, and online marketers might be interested in clickstream data that records a user's website navigation over time. Time series data must be cost-effectively collected in close to real-time, produced in extremely high volumes from a variety of sources, and stored in a way that makes it easy to organise and analyse.
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Ques. 7): How can I transmit information to Amazon Timestream?
Answer:
From linked devices, IT systems, and industrial machinery, you may gather time series data and write it into Amazon Timestream. Utilizing data gathering tools like AWS IoT Core, Amazon Kinesis Data Analytics for Apache Flink, or Telegraf, as well as the AWS SDKs, you can submit data to Amazon Timestream.
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Ques. 8): What advantages does the memory store offer?
Answer:
The memory store used by Amazon Timestream is a write-optimized store that receives and deduplicates time series data. Additionally, it accepts and handles data that arrives late from systems and programmes with erratic connectivity. For point-in-time queries that need low latency, the memory storage is additionally optimised.
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Ques. 9): Does Amazon Timestream support data encryption?
Answer:
In Amazon Timestream, data is always encrypted, whether at rest or in transit. Amazon Timestream also enables you to specify an AWS KMS customer managed key (CMK) for encrypting data in the magnetic store.
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Ques. 10): What is the operation of Amazon Timestream?
Answer:
The whole architecture of Amazon Timestream is focused on gathering, storing, and processing time series data. Because of the fully decoupled data intake, storage, and query processing systems supported by its serverless design, Amazon Timestream can provide almost infinite scale for your application's requirements. A Timestream table's schema is dynamically built based on the properties of the incoming time series data, allowing for flexible and gradual schema definition, as opposed to being pre-defined at table creation time. When data is saved, Amazon Timestream divides it into sections based on time and other factors, speeding up data access with a specially designed index. Additionally, by providing an in-memory storage for recent data, a magnetic store for historical data, and by allowing configurable rules to automatically migrate data from the memory store to the magnetic store as it reaches a specific age, Amazon Timestream automates the lifecycle management of data. As a result, you can quickly and effectively use SQL to extract insights from your data using Amazon Timestream's purpose-built adaptive query engine, which can seamlessly access and integrate data across storage tiers without requiring you to identify the data location. Finally, Amazon Timestream makes it simple for you to integrate Amazon Timestream into your time series solutions by integrating easily with your choice data collecting, visualisation, analytics, and machine learning services.
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Ques. 11): How do I handle data about upcoming or delayed arrivals?
Answer:
Data with a timestamp in the past is referred to as late arrival data. Data with a timestamp in the future is referred to as future data. Both types are accessible and storable via Amazon Timestream.
Simply enter the information to be stored as late arrival data into Amazon Timestream, and the service will choose whether to write it to the memory store or the magnetic store depending on the timestamp and the specified data retention for the memory and magnetic stores.
Model your data as a multi-measure record with the future timestamp represented as a measure inside the record if you want to store future data.
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Ques. 12): Does Amazon Timestream meet any compliance certification ready requirements?
Answer:
Amazon Timestream complies with the HIPAA Common Security Framework (CSF), the ISO (9001, 27001, 27017, and 27018), PCI DSS, FedRAMP (Moderate), and FedRAMP (High). Additionally, Amazon Timestream is covered by SOC 1 and SOC 2 and SOC 3 reports from AWS.
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Ques. 13): What is the price of Amazon Timestream?
Answer:
You only pay for the usage you make using Amazon Timestream. Writes, data storage, and data scanned by queries are all invoiced separately. Your writing, storage, and query capacities are automatically scaled by Amazon Timestream based on usage. Each table has a data retention policy that you may customise, and you can decide whether to save data magnetically or in memory.
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Ques. 14): What advantages does the magnetic store offer?
Answer:
The magnetic store offered by Amazon Timestream is a read-friendly repository for the past. Hundreds of terabytes of data can be quickly scanned by analytical queries that use the magnetic store's enhanced performance.
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Ques. 15): What machine learning (ML), analytics, and visualisation technologies are compatible with Amazon Timestream?
Answer:
Grafana and Amazon QuickSight are two tools you can use with Amazon Timestream to display and analyse time-series data. For your ML requirements, you can alternatively combine Amazon Timestream and Amazon SageMaker.
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Ques. 16): How do I use Grafana and Amazon Timestream?
Answer:
Grafana is a multi-platform, open-source analytics and interactive visualisation tool that you can use to view your Amazon Timestream time-series data and generate alerts.
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Ques. 17): What is the scaling of Amazon Timestream?
Answer:
Because of the fully decoupled data intake, storage, and query processing systems supported by Amazon Timestream's serverless architecture, which can scale separately, Amazon Timestream can provide essentially infinite scaling for your application's requirements.
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Ques. 18): How do I use Amazon Timestream to query data?
Answer:
Your time series data kept in Amazon Timestream can be queried using SQL. SQL can be used to perform interpolation, regression, and smoothing time series analytics functions.
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Ques. 19): How can I use Amazon Kinesis Data Analytics to transfer data to Amazon Timestream?
Answer:
Your time series data from Amazon Kinesis Data Analytics may be directly transferred into Amazon Timestream using Apache Flink.
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Ques. 20): What time stamp is applied to data when it is ingested by Amazon Timestream?
Answer:
The timestamp of the time series event being written into the database is what Amazon Timestream utilises. It provides nanosecond-level granularity timestamps.
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