Time series starts with InfluxDB

Join the millions of developers using InfluxDB to predict, respond, and adapt in real-time. InfluxDB 3 Core and Enterprise are now generally available, bringing the full power of InfluxDB 3 to production.

Get started with InfluxDB

InfluxDB 3 Enterprise

High-performance, fully featured, highly available database

curl -O https://www.influxdata.com/d/install_influxdb3.sh && sh install_influxdb3.sh enterprise

No credit card required. Trial for free.

Download for Windows Docs

InfluxDB 3 Core

Open source, single-node, high-speed recent data engine

curl -O https://www.influxdata.com/d/install_influxdb3.sh && sh install_influxdb3.sh

Download for Windows Docs

Developers choose InfluxDB

More downloads, more open source users, and a stronger community than any other time series database in the world.

1B+

Downloads of InfluxDB via Docker

1M+

Open source instances live today

5B+

Downloads of InfluxData’s Telegraf

2,800+

Contributors

Time series database
Source: DB Engines

Why InfluxDB

Manage high-volume, high-velocity data without sacrificing performance.

Unmatched performance at scale

Manage millions of time series data points per second without limits or caps.

Run where
you need it

Run at any scale in any environment: in the cloud, on-prem, or at the edge.

Build with the
tools you love

Easily connect to your tech stack using data standards and 300+ integrations.

Engineered for the entire lifecycle of time series data at a lower TCO

High-Speed
Ingest

Real-Time Querying
with SQL

Best-in-Category Compression

Bulk Data Eviction
and Transfer

See ways to get started

Why InfluxDB

Manage high-volume, high-velocity data without sacrificing performance.

Single Datastore

Run analytics across multiple workload types with a single purpose-built platform.

Columnar Design

Scales without limits with built-in storage and query performance optimization.

Native SQL

Query directly with InfluxDB using standard SQL.

Real-Time Query

Sub-second query response on leading-edge data.

Unlimited Cardinality

Analyze billions of time series data points per second without limitations or caps.

Superior Data Compression

Maximize data compression to store more data at a fraction of the cost.

Developers choose InfluxDB

More downloads, more open source users, and a larger community than any other time series database in the world.

1B+

Downloads of InfluxDB via Docker

1M+

Open source instances live today

2,800+

Contributors

5B+

Downloads of InfluxData’s Telegraf

Time series database
Source: DB Engines

Code in the languages you love

No need to conform to a new language or technology—InfluxDB supports multiple programming and query languages, with client libraries and integrations to make things simple, all powered by a RESTful API.

See All Integrations

              ReadWrite
            

                                    from influxdb_client_3 import InfluxDBClient3
import pandas
import os

database = os.getenv('INFLUX_DATABASE')
token = os.getenv('INFLUX_TOKEN')
host="https://us-east-1-1.aws.cloud2.influxdata.com"

def querySQL():
  client = InfluxDBClient3(host, database=database, token=token)
  table = client.query(
    '''SELECT
        room,
        DATE_BIN(INTERVAL '1 day', time) AS _time,
        AVG(temp) AS temp,
        AVG(hum) AS hum,
        AVG(co) AS co
      FROM home
      WHERE time >= now() - INTERVAL '90 days'
      GROUP BY room, _time
      ORDER BY _time'''
  )

  print(table.to_pandas().to_markdown())

  client.close()
  
querySQL()
                  
                  c
                

                                    from influxdb_client_3 import InfluxDBClient3
import os

database = os.getenv('INFLUX_DATABASE')
token = os.getenv('INFLUX_TOKEN')
host="https://us-east-1-1.aws.cloud2.influxdata.com"

def write_line_protocol():
  client = InfluxDBClient3(host, database=database, token=token)
  
  record = "home,room=Living\\ Room temp=22.2,hum=36.4,co=17i"
  
  print("Writing record:", record )
  client.write(record)
  
  client.close()

write_line_protocol()

                  
                  c
                

                                    @main
struct QueryCpuData: AsyncParsableCommand {
  @Option(name: .shortAndLong, help: "The name or id of the bucket destination.")
  private var bucket: String

  @Option(name: .shortAndLong, help: "The name or id of the organization destination.")
  private var org: String

  @Option(name: .shortAndLong, help: "Authentication token.")
  private var token: String

  @Option(name: .shortAndLong, help: "HTTP address of InfluxDB.")
  private var url: String
}

extension QueryCpuData {
  mutating func run() async throws {
    //
    // Initialize Client with default Bucket and Organization
    //
    let client = InfluxDBClient(
            url: url,
            token: token,
            options: InfluxDBClient.InfluxDBOptions(bucket: bucket, org: org))

    // Flux query
    let query = """
                from(bucket: "\(self.bucket)")
                    |> range(start: -10m)
                    |> filter(fn: (r) => r["_measurement"] == "cpu")
                    |> filter(fn: (r) => r["cpu"] == "cpu-total")
                    |> filter(fn: (r) => r["_field"] == "usage_user" or r["_field"] == "usage_system")
                    |> last()
                """

    print("\nQuery to execute:\n\(query)\n")

    let response = try await client.queryAPI.queryRaw(query: query)

    let csv = String(decoding: response, as: UTF8.self)
    print("InfluxDB response: \(csv)")

    client.close()
  }
}
                  
                  c
                

                                    import ArgumentParser
import Foundation
import InfluxDBSwift
import InfluxDBSwiftApis

@main
struct WriteData: AsyncParsableCommand {
  @Option(name: .shortAndLong, help: "The name or id of the bucket destination.")
  private var bucket: String

  @Option(name: .shortAndLong, help: "The name or id of the organization destination.")
  private var org: String

  @Option(name: .shortAndLong, help: "Authentication token.")
  private var token: String

  @Option(name: .shortAndLong, help: "HTTP address of InfluxDB.")
  private var url: String
}

extension WriteData {
  mutating func run() async throws {
    //
    // Initialize Client with default Bucket and Organization
    //
    let client = InfluxDBClient(
            url: url,
            token: token,
            options: InfluxDBClient.InfluxDBOptions(bucket: bucket, org: org))

    //
    // Record defined as Data Point
    //
    let recordPoint = InfluxDBClient
            .Point("demo")
            .addTag(key: "type", value: "point")
            .addField(key: "value", value: .int(2))
    //
    // Record defined as Data Point with Timestamp
    //
    let recordPointDate = InfluxDBClient
            .Point("demo")
            .addTag(key: "type", value: "point-timestamp")
            .addField(key: "value", value: .int(2))
            .time(time: .date(Date()))

    try await client.makeWriteAPI().write(points: [recordPoint, recordPointDate])
    print("Written data:\n\n\([recordPoint, recordPointDate].map { "\t- \($0)" }.joined(separator: "\n"))")
    print("\nSuccess!")

    client.close()
  }
}
                  
                  c
                

                                    import {InfluxDBClient} from '@influxdata/influxdb3-client'
import {tableFromArrays} from 'apache-arrow';

const database = process.env.INFLUX_DATABASE;
const token = process.env.INFLUX_TOKEN;
const host = "https://us-east-1-1.aws.cloud2.influxdata.com";

async function main() {
    const client = new InfluxDBClient({host, token})
    const query = `
    SELECT
      room,
      DATE_BIN(INTERVAL '1 day', time) AS _time,
      AVG(temp) AS temp,
      AVG(hum) AS hum,
      AVG(co) AS co
    FROM home
    WHERE time >= now() - INTERVAL '90 days'
    GROUP BY room, _time
    ORDER BY _time
    `
    const result = await client.query(query, database)

    const data = {room: [], day: [], temp: []}

    for await (const row of result) {
      data.day.push(new Date(row._time).toISOString())
      data.room.push(row.room)
      data.temp.push(row.temp)
    }

    console.table([...tableFromArrays(data)])

    client.close()
}

main()
                  
                  c
                

                                    import {InfluxDBClient} from '@influxdata/influxdb3-client'

const database = process.env.INFLUX_DATABASE;
const token = process.env.INFLUX_TOKEN;
const host = "https://us-east-1-1.aws.cloud2.influxdata.com";

async function main() {
    const client = new InfluxDBClient({host, token})

    const record = "home,room=Living\\ Room temp=22.2,hum=36.4,co=17i"
    await client.write(record, database)
    client.close()
}

main()
                  
                  c
                

                                    package com.influxdb3.examples;

import com.influxdb.v3.client.InfluxDBClient;
import java.util.stream.Stream;

public final class Query {
    private Query() {
        //not called
    }

    /**
     * @throws Exception
     */
    public static void main() throws Exception {

        final String hostUrl = "https://us-east-1-1.aws.cloud2.influxdata.com";
        final char[] authToken = (System.getenv("INFLUX_TOKEN")).toCharArray();
        final String database = System.getenv("INFLUX_DATABASE");

        try (InfluxDBClient client = InfluxDBClient.getInstance(hostUrl, authToken, database)) {
            String sql = """
                SELECT
                    room,
                    DATE_BIN(INTERVAL '1 day', time) AS _time,
                    AVG(temp) AS temp, AVG(hum) AS hum, AVG(co) AS co
                FROM home
                WHERE time >= now() - INTERVAL '90 days'
                GROUP BY room, _time
                ORDER BY _time""";

            String layoutHeading = "| %-16s | %-12s | %-6s |%n";
            System.out.printf("--------------------------------------------------------%n");
            System.out.printf(layoutHeading, "day", "room", "temp");
            System.out.printf("--------------------------------------------------------%n");

            String layout = "| %-16s | %-12s | %.2f |%n";
            try (Stream stream = client.query(sql)) {
                stream.forEach(row -> System.out.printf(layout, row[1], row[0], row[2]));
            }
        }
    }
}
                  
                  c
                

                                    package com.influxdb3.examples;

import com.influxdb.v3.client.InfluxDBClient;

public final class Write {

    public static void main() throws Exception {

        final String hostUrl = "https://us-east-1-1.aws.cloud2.influxdata.com";
        final char[] authToken = (System.getenv("INFLUX_TOKEN")).toCharArray();
        final String database = System.getenv("INFLUX_DATABASE");
        try (InfluxDBClient client = InfluxDBClient.getInstance(hostUrl, authToken, database)) {
            String record = "home,room=Living\\ Room temp=22.2,hum=36.4,co=17i";
            System.out.printf("Write record: %s%n", record);
            client.writeRecord(record);
        }
    }
}
                  
                  c
                

                                    InfluxDB2::Client.use('https://localhost:8086', 'my-token', org: 'my-org') do |client|

  result = client
    .create_query_api
    .query_raw(query: 'from(bucket:"my-bucket") |> range(start: 1970-01-01) |> last()')
  puts result
end
                  
                  c
                

                                    InfluxDB2::Client.use('https://localhost:8086', 'my-token',
                      bucket: 'my-bucket',
                      org: 'my-org',
                      precision: InfluxDB2::WritePrecision::NANOSECOND) do |client|

  write_api = client.create_write_api
  write_api.write(data: 'h2o,location=west value=33i 15')
end
                  
                  c
                

                                    package example

import org.apache.pekko.actor.ActorSystem
import org.apache.pekko.stream.scaladsl.Sink
import com.influxdb.client.scala.InfluxDBClientScalaFactory
import com.influxdb.query.FluxRecord

import scala.concurrent.Await
import scala.concurrent.duration.Duration

object InfluxDB2ScalaExample {

  implicit val system: ActorSystem = ActorSystem("it-tests")

  def main(args: Array[String]): Unit = {

    val influxDBClient = InfluxDBClientScalaFactory
      .create("http://localhost:8086", "my-token".toCharArray, "my-org")

    val fluxQuery = ("from(bucket: \"my-bucket\")\n"
      + " |> range(start: -1d)"
      + " |> filter(fn: (r) => (r[\"_measurement\"] == \"cpu\" and r[\"_field\"] == \"usage_system\"))")

    //Result is returned as a stream
    val results = influxDBClient.getQueryScalaApi().query(fluxQuery)

    //Example of additional result stream processing on client side
    val sink = results
      //filter on client side using `filter` built-in operator
      .filter(it => "cpu0" == it.getValueByKey("cpu"))
      //take first 20 records
      .take(20)
      //print results
      .runWith(Sink.foreach[FluxRecord](it => println(s"Measurement: ${it.getMeasurement}, value: ${it.getValue}")
      ))

    // wait to finish
    Await.result(sink, Duration.Inf)

    influxDBClient.close()
    system.terminate()
  }
}
                  
                  c
                

                                       
package com.influxdb.client.scala.internal

import org.apache.pekko.Done
import org.apache.pekko.stream.scaladsl.{Flow, Keep, Sink, Source}
import com.influxdb.client.InfluxDBClientOptions
import com.influxdb.client.domain.WritePrecision
import com.influxdb.client.internal.{AbstractWriteBlockingClient, AbstractWriteClient}
import com.influxdb.client.scala.WriteScalaApi
import com.influxdb.client.service.WriteService
import com.influxdb.client.write.{Point, WriteParameters}

import javax.annotation.Nonnull
import scala.collection.immutable.ListMap
import scala.concurrent.Future
import scala.jdk.CollectionConverters._

class WriteScalaApiImpl(@Nonnull service: WriteService, @Nonnull options: InfluxDBClientOptions)

  extends AbstractWriteBlockingClient(service, options) with WriteScalaApi {

  override def writeRecord(precision: Option[WritePrecision], bucket: Option[String], org: Option[String]): Sink[String, Future[Done]] = {
    Flow[String]
      .map(record => Seq(new AbstractWriteClient.BatchWriteDataRecord(record)))
      .toMat(Sink.foreach(batch => writeHttp(precision, bucket, org, batch)))(Keep.right)
  }

  override def writeRecords(precision: Option[WritePrecision], bucket: Option[String], org: Option[String]): Sink[Seq[String], Future[Done]] = {
    writeRecords(toWriteParameters(precision, bucket, org))
  }

  override def writeRecords(parameters: WriteParameters): Sink[Seq[String], Future[Done]] = {
    Flow[Seq[String]]
      .map(records => records.map(record => new AbstractWriteClient.BatchWriteDataRecord(record)))
      .toMat(Sink.foreach(batch => writeHttp(parameters, batch)))(Keep.right)
  }


  override def writePoint(bucket: Option[String], org: Option[String]): Sink[Point, Future[Done]] = {
    Flow[Point]
      .map(point => (point.getPrecision, Seq(new AbstractWriteClient.BatchWriteDataPoint(point, options))))
      .toMat(Sink.foreach(batch => writeHttp(Some(batch._1), bucket, org, batch._2)))(Keep.right)
  }

  override def writePoints(bucket: Option[String], org: Option[String]): Sink[Seq[Point], Future[Done]] = {
    writePoints(new WriteParameters(bucket.orNull, org.orNull, null, null))
  }

  override def writePoints(parameters: WriteParameters): Sink[Seq[Point], Future[Done]] = {
    Flow[Seq[Point]]
      // create ordered Map
      .map(records => records.foldRight(ListMap.empty[WritePrecision, Seq[Point]]) {
        case (point, map) => map.updated(point.getPrecision, point +: map.getOrElse(point.getPrecision, Seq()))
      }.toList.reverse)
      .map(grouped => grouped.map(group => (group._1, group._2.map(point => new AbstractWriteClient.BatchWriteDataPoint(point, options)))))
      .flatMapConcat(batches => Source(batches))
      .toMat(Sink.foreach(batch => writeHttp(parameters.copy(batch._1, options), batch._2)))(Keep.right)
  }

  override def writeMeasurement[M](precision: Option[WritePrecision], bucket: Option[String], org: Option[String]): Sink[M, Future[Done]] = {
    Flow[M]
      .map(measurement => {
        val parameters = toWriteParameters(precision, bucket, org)
        Seq(toMeasurementBatch(measurement, parameters.precisionSafe(options)))
      })
      .toMat(Sink.foreach(batch => writeHttp(precision, bucket, org, batch)))(Keep.right)
  }

  override def writeMeasurements[M](precision: Option[WritePrecision], bucket: Option[String], org: Option[String]): Sink[Seq[M], Future[Done]] = {
    writeMeasurements(toWriteParameters(precision, bucket, org))
  }

  override def writeMeasurements[M](parameters: WriteParameters): Sink[Seq[M], Future[Done]] = {
    Flow[Seq[M]]
      .map(records => records.map(record => toMeasurementBatch(record, parameters.precisionSafe(options))))
      .toMat(Sink.foreach(batch => writeHttp(parameters, batch)))(Keep.right)
  }

  private def writeHttp(precision: Option[WritePrecision], bucket: Option[String], org: Option[String], batch: Seq[AbstractWriteClient.BatchWriteData]): Done = {
    writeHttp(toWriteParameters(precision, bucket, org), batch)
  }

  private def writeHttp(parameters: WriteParameters, batch: Seq[AbstractWriteClient.BatchWriteData]): Done = {
    write(parameters, batch.toList.asJava.stream())
    Done.done()
  }

  private def toWriteParameters(precision: Option[WritePrecision], bucket: Option[String], org: Option[String]): WriteParameters = {
    val parameters = new WriteParameters(bucket.orNull, org.orNull, precision.orNull, null)
    parameters.check(options)
    parameters
  }
}

  


                  
                  c
                

                                    package influxdbv3

import (
  "context"
  "fmt"
  "io"
  "os"
  "text/tabwriter"

  "github.com/apache/arrow/go/v12/arrow"
  "github.com/InfluxCommunity/influxdb3-go/influx"
)

func QuerySQL() error {
  url := "https://us-east-1-1.aws.cloud2.influxdata.com"
  token := os.Getenv("INFLUX_TOKEN")
  database := os.Getenv("INFLUX_DATABASE")
	
  client, err := influx.New(influx.Configs{
	HostURL: url,
	AuthToken: token,
  })

  defer func (client *influx.Client)  {
	err := client.Close()
	if err != nil {
		panic(err)
	}
  }(client)

  query := `
    SELECT
	  room,
	  DATE_BIN(INTERVAL '1 day', time) AS _time,
	  AVG(temp) AS temp,
	  AVG(hum) AS hum,
	  AVG(co) AS co
	FROM home
	WHERE time >= now() - INTERVAL '90 days'
	GROUP BY room, _time
	ORDER BY _time
`

  iterator, err := client.Query(context.Background(), database, query)

  if err != nil {
    panic(err)
  }

  w := tabwriter.NewWriter(io.Discard, 4, 4, 1, ' ', 0)
  w.Init(os.Stdout, 0, 8, 0, '\t', 0)
  fmt.Fprintln(w, "day\troom\ttemp")

  for iterator.Next() {
	row := iterator.Value()
	day := (row["_time"].(arrow.Timestamp)).ToTime(arrow.TimeUnit(arrow.Nanosecond))
	fmt.Fprintf(w, "%s\t%s\t%.2f\n", day, row["room"], row["temp"])
  }

  w.Flush()
  return nil
}
                  
                  c
                

                                    package influxdbv3

import (
  "context"
  "os"
  "fmt"
  "github.com/InfluxCommunity/influxdb3-go/influx"
)

func WriteLineProtocol() error {
  url := "https://us-east-1-1.aws.cloud2.influxdata.com"
  token := os.Getenv("INFLUX_TOKEN")
  database := os.Getenv("INFLUX_DATABASE")
	
  client, err := influx.New(influx.Configs{
	HostURL: url,
	AuthToken: token,
  })

  defer func (client *influx.Client)  {
	err := client.Close()
	if err != nil {
		panic(err)
	}
  }(client)

  record := "home,room=Living\\ Room temp=22.2,hum=36.4,co=17i"
  fmt.Println("Writing record: ", record)
  err = client.Write(context.Background(), database, []byte(record))

  if err != nil {
    panic(err)
  }
  return nil
}
                  
                  c
                

                                    using System;
using System.Threading.Tasks;
using InfluxDB3.Client;
using InfluxDB3.Client.Query;

namespace InfluxDBv3;

public class Query
{
  static async Task QuerySQL()
  {
    const string hostUrl = "https://us-east-1-1.aws.cloud2.influxdata.com";
    string? database = System.Environment.GetEnvironmentVariable("INFLUX_DATABASE");
    string? authToken = System.Environment.GetEnvironmentVariable("INFLUX_TOKEN");

    using var client = new InfluxDBClient(hostUrl, authToken: authToken, database: database);
  
    const string sql = @"
      SELECT
        room,
        DATE_BIN(INTERVAL '1 day', time) AS _time,
        AVG(temp) AS temp,
        AVG(hum) AS hum,
        AVG(co) AS co
      FROM home
      WHERE time >= now() - INTERVAL '90 days'
      GROUP BY room, _time
      ORDER BY _time
    ";

    Console.WriteLine("{0,-30}{1,-15}{2,-15}", "day", "room", "temp");
    await foreach (var row in client.Query(query: sql))
    {
      Console.WriteLine("{0,-30}{1,-15}{2,-15}", row[1], row[0], row[2]);
    }

    Console.WriteLine();
  }
}
                  
                  c
                

                                    using System;
using System.Threading.Tasks;
using InfluxDB3.Client;
using InfluxDB3.Client.Query;

namespace InfluxDBv3;

public class Write
{
  public static async Task WriteLineProtocol()
  {
    const string hostUrl = "https://us-east-1-1.aws.cloud2.influxdata.com";
    string? database = System.Environment.GetEnvironmentVariable("INFLUX_DATABASE");
    string? authToken = System.Environment.GetEnvironmentVariable("INFLUX_TOKEN");

    using var client = new InfluxDBClient(hostUrl, authToken: authToken, database: database);

    const string record = "home,room=Living\\ Room temp=22.2,hum=36.4,co=17i";
    Console.WriteLine("Write record: {0,-30}", record);
    await client.WriteRecordAsync(record: record);
  }
}

                  
                  c
                

                                    client <- InfluxDBClient$new(url = "http://localhost:8086",
                             token = "my-token",
                             org = "my-org")
                            
data <- client$query('from(bucket: "my-bucket") |> range(start: -1h) |> drop(columns: ["_start", "_stop"])')
data
                  
                  c
                

                                    client <- InfluxDBClient$new(url = "http://localhost:8086",
                             token = "my-token",
                             org = "my-org")
data <- ...
response <- client$write(data, bucket = "my-bucket", precision = "us",
                         measurementCol = "name",
                         tagCols = c("region", "sensor_id"),
                         fieldCols = c("altitude", "temperature"),
                         timeCol = "time")
                  
                  c
                

Read documentation for v2 Read documentation for v3

Loved by developers, trusted by enterprises

65M+

daly events processed

45x

more resource efficient

Siemens Energy

“[We] long used InfluxDB open source... Moving to commercial InfluxDB was a strategic move to unify our data infrastructure, ensuring we have the reliability, scalability, and real-time performance to keep pace with production... and proactive maintenance.”

Jan Petersen
Senior Manufacturing Engineer

Read Case Study

500M+

Metrics collected daily

Mission-critical monitoring

Real-time data access for queries

LOFT ORBITAL

Space Made Simple: How Loft Orbital Delivers Unparalleled Speed-to-Space with InfluxDB Cloud

Read Case Study

65M+

daly events processed

45x

more resource efficient

CAPITAL ONE

"InfluxDB is a high-speed read and write database. The data is written in real-time, you can read it in real-time, and while reading, you can apply your machine learning model. So, in real-time, you can forecast and detect anomalies."

Rajeev Tomer
Sr. Manager of Data Engineering

Read Case Study

50%

lower total cost of ownership

100K

real-time metrics with simplified deployment

TERÉGA

Teréga Replaced Its Legacy Data Historian with InfluxDB

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Lorem ipsum dolor sit amet consectetur.

Read Case Study

65M+

daly events processed

45x

more resource efficient

WIDEOPENWEST

"I was blown away with how easy it was to install and configure InfluxDB. The clustering was easy. The documentation was great, and the support has been second to none."

Dylan Shorter
Engineer III, Software and Product Integration Engineering

Read Case Study

45%

Less equipment downtime

10%

Reduced waste

MAJIK SYSTEMS

From Reactive to Proactive: How MAJiK Systems Embraced Predictive Maintenance with InfluxDB and Time Series Data

Dylan Shorter
Engineer III, Software and Product Integration Engineering

Read Case Study

65M+

daly events processed

45x

more resource efficient

JU:NIZ ENERGY

“With InfluxDB Cloud Dedicated, the great thing is that we don't need to think about data storage costs or usage anymore because data storage gets way cheaper.”

Ricardo Kissinger
Head of IT Infrastructure and IT Security

Read Case Study

InfluxDB is a G2 Leader in Time Series

“InfluxDB is a strong database built specifically for time series data. It has made working with such data seamless and easy.”
— Verified G2 reviewer

Read reviews

INDUSTRIES

Time series analytics for every industry

Today, InfluxDB deployments span multiple industries, with customers running at scale in any environment—public and private cloud, on-premises, and at the edge.

Manufacturing

Energy and Utilities

Telecommunications

Consumer IoT

Industrial IoT

Aerospace

Manufacturing

Analyze production data streams in real-time to identify bottlenecks, prevent downtime, and power predictive maintenance for your industrial equipment.

Learn More

Energy and Utilities

Monitor, optimize, and manage renewable energy and traditional power systems to achieve smart grid balancing and optimization. Forecast and predict maintenance needs for renewable energy sources, such as wind turbines and solar farms.

Learn More

Telecommunications

Analyze network performance and usage patterns in telecommunication infrastructure. Improve quality of service, optimize infrastructure resources, and reduce operational costs.

Learn More

Consumer IoT

Use real-time monitoring and analytics to optimize energy consumption and improve quality of life.

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Industrial IoT

Optimize operational processes with real-time insights from industrial equipment and sensors.

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Aerospace

Get real-time insights from satellites, networks, and every stage of the launch operation process. Reduce errors and accelerate time to market in this mission-critical space.

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Manufacturing

Analyze production data streams in real-time to identify bottlenecks, prevent downtime and power predictive maintenance for your industrial equipment.

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Energy and Utilities

Monitor, optimize, and manage renewable energy and traditional power systems to achieve smart grid balancing and optimization. Forecast and predict maintenance needs for your renewable energy sources such as wind turbines and solar farms.

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Telecommunications

Analyze network performance and usage patterns in telecommunication infrastructure. Improve quality of service, optimize infrastructure resources, and reduce operational costs.

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Consumer IoT

Use real-time monitoring and analytics to optimize energy consumption and improve quality of life.

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Industrial IoT

Optimize operational processes with real-time insights from industrial equipment and sensors.

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Aerospace

Get real-time insights from satellites, networks, and every stage of the launch operation process. Reduce errors and accelerate time to market in this mission-critical space.

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Analyze time series data from any workload

Real-Time Analytics

Query data immediately upon arrival for real-time insights across systems and applications

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Network and Device Telemetry

Monitor and control devices and sensors in IoT, network, and field deployments.

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Modern Data Historian

Unleash and transform on-site industrial OT data in the manufacturing plant and on the factory floor.

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Machine Learning and AI

Share real-time data with open ML/AI tools to create predictive analytics for automated, intelligent applications and systems.

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Real-Time Analytics

Query data immediately upon arrival for real-time insights across systems and applications.

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Network & Device Telemetry

Monitor and control devices and sensors in IoT, network, and field deployments.

Learn More

Modern Data Historian

Unleash and transform on-site industrial OT data in the manufacturing plant and on the factory floor.

Learn More

Machine Learning & AI

Share real-time data with open ML/AI tools to create predictive analytics for intelligent applications and systems.

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Manufacturing

Energy and Utilities

Telecommunications

Consumer IoT

Industrial IoT

Aerospace

Analyze time series data from any workload

Real-Time Analytics

Network and Device Telemetry

Modern Data Historian

Machine Learning and AI

Real-Time Analytics

Network & Device Telemetry

Modern Data Historian

Machine Learning & AI

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