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"https://schema.org", "@type": "TechArticle", "headline": "Time Series & Objects: Using Data on the Edge", "description": "Technical blog post on ObjectBox technology.", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://objectbox.io/time-series-and-objects-using-data-on-the-edge/" }, "author": { "@type": "Organization", "name": "ObjectBox", "url": "https://objectbox.io" }, "publisher": { "@type": "Organization", "name": "ObjectBox", "logo": { "@type": "ImageObject", "url": "https://objectbox.io/wp-content/uploads/2023/01/objectbox-logo.png" } }, "datePublished": "2020-06-30T11:26:36+01:00", "dateModified": "2020-10-17T11:52:52+01:00", "proficiencyLevel": "Expert", "dependencies": "ObjectBox", "keywords": "Edge Computing, iot, objects, Release, time series", "image": "https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-6.jpg", "articleBody": "Many IoT projects collect, both time series data and other types of data. Typically, this means they will run two databases: A time-series database and a traditional database or key/value store. This creates fracture and overhead, which is why ObjectBox TS brings together the best of both worlds in one database (DB). ObjectBox TS is a hybrid database: an extremely fast object-oriented DB plus a time-series extension, specially optimized for time series data. In combination with its tiny footprint, ObjectBox is a perfect match for IoT applications running on the edge. The out-of-the-box synchronization takes care of synchronizing selected data sets super efficiently and it works offline and online, on-premise, in the cloud.\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tWhat is time series data?\nThere are a lot of different types of data that are used in IoT applications. Time-series is one of the most common data types in analytics, high-frequency inspections, and maintenance applications for IIoT / Industry 4.0 and smart mobility. Time series tracks data points over time, most often taken at equally spaced intervals. Typical data sources are sensor data, events, clicks, temperature \u2013 anything that changes over time.\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tWhy use time series data on the edge?\nTime-series data sets are usually collected from a lot of sensors, which sample at a high rate \u2013 which means that a lot of data is being collected.\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tFor example, if a Raspberry Pi gateway collects 20 data points/second, typically that would mean 1200 entries a minute measuring e.g. 32 degrees. As temperatures rarely change significantly in short time frames, does all of this data need to go to the cloud? Unless you need to know the exact temperature in a central location every millisecond, the answer is no. Sending all data to the cloud is a waste of resources, causing high cloud costs without providing immediate, real-time insights.\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tThe Best of Both Worlds: time series + object oriented data persistence\nWith ObjectBox you aren\u2019t limited to only using time series data. ObjectBox TS is optimized for time series data, but ObjectBox is a robust object oriented database solution that can store any data type. With ObjectBox, model your world in objects and combine this with the power of time-series data to identify patterns in your data, on the device, in real time. By combining time series data with more complex data types, ObjectBox empowers new use cases on the edge based on a fast and easy all-in-one data persistence solution.\u00a0\nBring together different data streams for a fusion of data; mix and match sensor data with the ObjectBox time series dashboard and find patterns in your data. On top, ObjectBox takes care of synchronizing selected data between devices (cloud / on-premise) efficiently for you.\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tGet a complete picture of your data in one place\n\nUse Case: Automotive (Process Optimization)\nMost manufacturers, whether they’re producing cars, the food industry, or utilities, have already been optimizing production for a long period of time. However, there are still many cases and reasons why costly manual processes prevail.\u00a0 One such example is automotive varnish. In some cases, while the inspection is automatic and intelligent, a lot of cars need to be touched up by hand, because the factors leading to the errors in the paint are not yet discovered. While there is a lot of internal expert know-how available from the factory workers, their gut feel is typically not enough to adapt production processes.\nHow can this be improved using time series and object data?\u00a0\nThe cars (objects) are typically already persisted including all the mass customization and model information. If now, all data, including sensor data, of the manufacturing site like temperature, humidity, spray speed (all time-series data) is persisted and added to each car object, any kind of correlations between production site variables, individual car properties and varnish quality can be detected. Over time, patterns will emerge. The gut feel of the factory workers would provide a great starting point for analyzing the data to discover Quick Wins before longterm patterns can be detected. Over time, AI and automatic learning kicks in to optimize the factory setup best possible to reduce the need for paint touch ups as much as possible.\u00a0\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tUse Case: Smart Grids\nUtility grid loads shift continually throughout the day, effecting grid efficiency, pricing, and energy delivery. Using Smart Grids, utilities companies can increase efficiency and reliability in real time. In order to get insights from Smart Grids, companies need to collect a large volume of data from existing systems. A huge portion of this data is time series, e.g. usage and load statistics. On top, they incorporate other forms of data, e.g. asset relationship data, weather conditions, and customer profiles. Using visualization and analytical tools, these data types can be brought together to generate business insights and actionable operative goals.\n\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tObjectBox TS: time series with objects\nStoring and processing both time series data and objects on the edge, developers can gather complex data sets and get real time insight, even when offline. Combining these data types gives a fuller understanding and context for data – not only what happens over time, but what other factors could be influencing results. Using a fast hybrid edge database allows developers to save resources, while maintaining speed and efficiency. By synchronizing useful data to the cloud, real time data can be used for both immediate action, and post-event analysis.\nGet in touch with our team to get a virtual demo of ObjectBox TS, or check out the sample GitHub repo to see more about the code.\n\t\t\t\n\t\t\t\tGo to ObjectBox TS", "wordCount": 920, "inLanguage": "en-US", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "breadcrumb": { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://objectbox.io" }, { "@type": "ListItem", "position": 2, "name": "Blog", "item": "https://objectbox.io/blog/" }, { "@type": "ListItem", "position": 3, "name": "Edge Computing", "item": "https://objectbox.io/category/edge-computing/" }, { "@type": "ListItem", "position": 4, "name": "Time Series & Objects: Using Data on the Edge", "item": "https://objectbox.io/time-series-and-objects-using-data-on-the-edge/" } ] } }</script><script type="application/ld+json">{ "@type": "Citation", "url": "https://objectbox.io/why-do-we-need-edge-computing-for-a-sustainable-future/", "name": "waste of resources", "description": "Reference 1 for Time Series & Objects: Using Data on the Edge" }</script><meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0" /><style type="text/css" id="tve_global_variables">:root{--tcb-color-0:rgb(125, 220, 125);--tcb-color-0-h:120;--tcb-color-0-s:57%;--tcb-color-0-l:67%;--tcb-color-0-a:1;--tcb-gradient-0:linear-gradient(54deg, rgb(230, 25, 85) 0%, rgb(245, 150, 45) 64%, rgb(249, 229, 5) 100%, var(--tcb-color-0) 100%);--tcb-background-author-image:url(https://secure.gravatar.com/avatar/f5b9dacdc7f63c622a4d6ac2833705b8?s=256&d=mm&r=g);--tcb-background-user-image:url();--tcb-background-featured-image-thumbnail:url(https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-6.jpg);}</style><style type="text/css" id="custom-background-css"> body.custom-background { background-color: #f7f7f7; } </style> <script id='nitro-telemetry-meta' nitro-exclude>window.NPTelemetryMetadata={missReason: (!window.NITROPACK_STATE ? 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et_pb_button_1_tb_header_wrapper et_pb_button_alignment_center et_pb_module "> <a class="et_pb_button et_pb_button_1_tb_header et_pb_bg_layout_dark" href="https://objectbox.io/offline-first-mobile-database/">Get started</a> </div> </div> </div> </div> </div> </header> <div id="et-main-area"> <div id="main-content"> <div class="container"> <div id="content-area" class="clearfix"> <div id="left-area"> <article id="post-50789" class="et_pb_post post-50789 post type-post status-publish format-standard has-post-thumbnail hentry category-edge-computing category-insights category-iot category-release category-time-series tag-edge-computing tag-iot tag-objects tag-release tag-time-series"> <div class="et_post_meta_wrapper"> <h1 class="entry-title">Time Series & Objects: Using Data on the Edge</h1> <p class="post-meta"><span class="published">Jun 30, 2020</span> | <a href="https://objectbox.io/category/edge-computing/" rel="category tag">Edge Computing</a>, <a href="https://objectbox.io/category/insights/" rel="category tag">Insights</a>, <a href="https://objectbox.io/category/iot/" rel="category tag">IoT</a>, <a href="https://objectbox.io/category/release/" rel="category tag">Release</a>, <a href="https://objectbox.io/category/time-series/" rel="category tag">Time Series</a></p> </div> <div class="entry-content"> <div class="et-l et-l--post"> <div class="et_builder_inner_content et_pb_gutters3"><div class="et_pb_section et_pb_section_5 et_section_regular" > <div class="et_pb_row et_pb_row_30"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_40 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_40 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><p><span style="font-weight: 400;">Many IoT projects collect, both time series data </span><i><span style="font-weight: 400;">and </span></i><span style="font-weight: 400;">other types of data. Typically, this means they will run two databases: A time-series database and a traditional database or key/value store. This creates fracture and overhead, which is why <a href="https://objectbox.io/time-series-database/" target="_blank" rel="noopener noreferrer">ObjectBox TS</a> brings together the best of both worlds in one database (DB). ObjectBox TS is a hybrid database: an extremely fast object-oriented DB plus a time-series extension, specially optimized for time series data. In combination with its tiny footprint, ObjectBox is a perfect match for IoT applications running on the edge. The out-of-the-box synchronization takes care of synchronizing selected data sets super efficiently and it works offline and online, on-premise, in the cloud.<br /></span></p></div> </div> </div> </div><div class="et_pb_row et_pb_row_31"> <div class="et_pb_column et_pb_column_1_3 et_pb_column_41 et_pb_css_mix_blend_mode_passthrough"> <div class="et_pb_module et_pb_image et_pb_image_15"> <span class="et_pb_image_wrap "><picture loading="lazy" decoding="async" data-lazy-type="image" title="time-series-data-temperature" class="lazy lazy-hidden wp-image-50801"> <source type="image/webp" data-srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-data-temperature.png.webp" srcset="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif"/> <img loading="lazy" decoding="async" width="1075" height="527" src="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif" data-lazy-type="image" data-src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-data-temperature.png" alt="time-series-data-example-temperature"/> </picture> <noscript><picture loading="lazy" decoding="async" title="time-series-data-temperature" class="wp-image-50801"> <source type="image/webp" srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-data-temperature.png.webp"/> <img loading="lazy" decoding="async" width="1075" height="527" src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/time-series-data-temperature.png" alt="time-series-data-example-temperature"/> </picture> </noscript></span> </div> </div><div class="et_pb_column et_pb_column_2_3 et_pb_column_42 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_41 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h3><span style="font-weight: 400;">What is time series data?</span></h3> <p><span style="font-weight: 400;">There are a lot of different types of data that are used in IoT applications. Time-series is one of the most common data types in analytics, high-frequency inspections, and maintenance applications for <a href="https://objectbox.io/industrial-iot/">IIoT / Industry 4.0</a> and <a href="https://objectbox.io/smart-mobility/">smart mobility</a>. Time series tracks data points</span><a href="https://en.wikipedia.org/wiki/Time_series"><span style="font-weight: 400;"> over time</span></a><span style="font-weight: 400;">, most often taken at equally spaced intervals. Typical data sources are sensor data, events, clicks, temperature – anything that changes over time.</span></p></div> </div> </div> </div><div class="et_pb_row et_pb_row_32"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_43 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_42 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h3><span style="font-weight: 400;">Why use time series data on the edge?</span></h3> <p><span style="font-weight: 400;">Time-series data sets are usually collected from a lot of sensors, which sample at a high rate – which means that a lot of data is being collected.</span></p></div> </div> </div> </div><div class="et_pb_row et_pb_row_33"> <div class="et_pb_column et_pb_column_1_3 et_pb_column_44 et_pb_css_mix_blend_mode_passthrough"> <div class="et_pb_module et_pb_text et_pb_text_43 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><p><span style="font-weight: 400;">For example, if a Raspberry Pi gateway collects 20 data points/second, typically that would mean 1200 entries a minute measuring e.g. 32 degrees. As temperatures rarely change significantly in short time frames, does all of this data need to go to the cloud? Unless you need to know the exact temperature in a central location every millisecond, the answer is no. Sending all data to the cloud is a</span><a href="https://objectbox.io/why-do-we-need-edge-computing-for-a-sustainable-future/"><span style="font-weight: 400;"> waste of resources</span></a><span style="font-weight: 400;">, causing high cloud costs without providing immediate, real-time insights.</span></p></div> </div> </div><div class="et_pb_column et_pb_column_2_3 et_pb_column_45 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_image et_pb_image_16"> <span class="et_pb_image_wrap "><picture loading="lazy" decoding="async" data-lazy-type="image" title="timeseries objects" class="lazy lazy-hidden wp-image-50807"> <source type="image/webp" data-srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/timeseries.png.webp" srcset="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif"/> <img loading="lazy" decoding="async" width="1831" height="747" src="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif" data-lazy-type="image" data-src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/timeseries.png" alt="time-series-objects-edge"/> </picture> <noscript><picture loading="lazy" decoding="async" title="timeseries objects" class="wp-image-50807"> <source type="image/webp" srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/timeseries.png.webp"/> <img loading="lazy" decoding="async" width="1831" height="747" src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/timeseries.png" alt="time-series-objects-edge"/> </picture> </noscript></span> </div> </div> </div><div class="et_pb_row et_pb_row_34"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_46 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_44 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h3><span style="font-weight: 400;">The Best of Both Worlds: time series + object oriented data persistence</span></h3> <p><span style="font-weight: 400;">With ObjectBox you aren’t limited to only using time series data. </span><a href="https://objectbox.io/time-series-database"><span style="font-weight: 400;">ObjectBox TS</span></a><span style="font-weight: 400;"> is optimized for time series data, but ObjectBox is a robust </span><a href="https://en.wikipedia.org/wiki/Object-oriented_programming"><span style="font-weight: 400;">object oriented</span></a><span style="font-weight: 400;"> database solution that can store </span><b>any</b><span style="font-weight: 400;"> data type. With ObjectBox, model your world in objects and combine this with the power of time-series data to identify patterns in your data, on the device, in real time. By combining time series data with more complex data types, ObjectBox empowers new use cases</span><a href="https://objectbox.io/what-is-edge-computing/"><span style="font-weight: 400;"> on the edge</span></a><span style="font-weight: 400;"> based on a fast and easy all-in-one data persistence solution. </span></p> <p><span style="font-weight: 400;">Bring together different data streams for a fusion of data; mix and match sensor data with the ObjectBox time series dashboard and find patterns in your data. On top, ObjectBox takes care of synchronizing selected data between devices (cloud / on-premise) </span><a href="https://objectbox.io/sync"><span style="font-weight: 400;">efficiently</span></a><span style="font-weight: 400;"> for you.</span></p></div> </div><div class="et_pb_module et_pb_image et_pb_image_17"> <span class="et_pb_image_wrap "><picture loading="lazy" decoding="async" data-lazy-type="image" title="time series objects" class="lazy lazy-hidden wp-image-50693"> <source type="image/webp" data-srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/image8.png.webp" srcset="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif"/> <img loading="lazy" decoding="async" width="1448" height="489" src="//objectbox.io/wordpress/wp-content/plugins/a3-lazy-load/assets/images/lazy_placeholder.gif" data-lazy-type="image" data-src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/image8.png" alt="time-series-data-visualization-dashboard"/> </picture> <noscript><picture loading="lazy" decoding="async" title="time series objects" class="wp-image-50693"> <source type="image/webp" srcset="https://objectbox.io/wordpress/wp-content/uploads/2020/06/image8.png.webp"/> <img loading="lazy" decoding="async" width="1448" height="489" src="https://objectbox.io/wordpress/wp-content/uploads/2020/06/image8.png" alt="time-series-data-visualization-dashboard"/> </picture> </noscript></span> </div> </div> </div><div class="et_pb_row et_pb_row_35"> <div class="et_pb_column et_pb_column_4_4 et_pb_column_47 et_pb_css_mix_blend_mode_passthrough et-last-child"> <div class="et_pb_module et_pb_text et_pb_text_45 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h2><span style="font-weight: 400;"></span><span style="font-weight: 400;">Get a complete picture of your data in one place</span></h2> <p><span style="font-weight: 400;"></span></p> <h3><span style="font-weight: 400;">Use Case: Automotive (Process Optimization)<br /></span></h3> <p><span style="font-weight: 400;">Most manufacturers, whether they’re producing cars, the food industry, or utilities, have already been optimizing production for a long period of time. However, there are still many cases and reasons why costly manual processes prevail. One such example is automotive varnish. In some cases, while the inspection is automatic and intelligent, a lot of cars need to be touched up by hand, because the factors leading to the errors in the paint are not yet discovered. While there is a lot of internal expert know-how available from the factory workers, their gut feel is typically not enough to adapt production processes.</span></p> <p><span style="font-weight: 400;">How can this be improved using time series and object data? </span></p> <p><span style="font-weight: 400;">The cars (objects) are typically already persisted including all the mass customization and model information. If now, all data, including sensor data, of the manufacturing site like temperature, humidity, spray speed (all time-series data) is persisted and added to each car object, any kind of correlations between production site variables, individual car properties and varnish quality can be detected. Over time, patterns will emerge. The gut feel of the factory workers would provide a great starting point for analyzing the data to discover Quick Wins before longterm patterns can be detected. Over time, AI and automatic learning kicks in to optimize the factory setup best possible to reduce the need for paint touch ups as much as possible. </span></p></div> </div><div class="et_pb_module et_pb_text et_pb_text_46 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h3><span style="font-weight: 400;">Use Case: Smart Grids</span></h3> <p><span style="font-weight: 400;">Utility grid loads shift continually throughout the day, effecting grid efficiency, pricing, and energy delivery. Using Smart Grids, utilities companies can increase efficiency and reliability in real time. In order to get insights from Smart Grids, companies need to collect a large volume of data from existing systems. A huge portion of this data is time series, e.g. usage and load statistics. On top, they incorporate other forms of data, e.g. asset relationship data, weather conditions, and customer profiles. Using visualization and analytical tools, these data types can be brought together to generate business insights and actionable operative goals.</span></p></div> </div><div class="et_pb_module et_pb_text et_pb_text_47 et_pb_text_align_left et_pb_bg_layout_light"> <div class="et_pb_text_inner"><h2><span style="font-weight: 400;">ObjectBox TS: time series with objects<br /></span></h2> <p>Storing and processing both time series data and objects on the edge, developers can gather complex data sets and get real time insight, even when offline. Combining these data types gives a fuller understanding and context for data – not only what happens over time, but what other factors could be influencing results. Using a fast hybrid edge database allows developers to save resources, while maintaining speed and efficiency. By synchronizing useful data to the cloud, real time data can be used for both immediate action, and post-event analysis.</p> <p><a href="/contact">Get in touch with our team</a> to get a virtual demo of ObjectBox TS, or check out the <a href="https://github.com/objectbox/objectbox-ts-demo">sample GitHub repo</a> to see more about the code.</p></div> </div><div class="et_pb_button_module_wrapper et_pb_button_5_wrapper et_pb_button_alignment_center et_pb_module "> <a class="et_pb_button et_pb_button_5 et_pb_bg_layout_light" href="https://objectbox.io/time-series-database/" target="_blank" data-icon="$">Go to ObjectBox TS</a> </div> </div> </div> </div> </div> </div> </div> <div class="et_post_meta_wrapper"> </div> </article> </div> </div> </div> </div> <script nitro-exclude> var heartbeatData = new FormData(); heartbeatData.append('nitroHeartbeat', '1'); fetch(location.href, {method: 'POST', body: heartbeatData, credentials: 'omit'}); </script> <script nitro-exclude> document.cookie = 'nitroCachedPage=' + (!window.NITROPACK_STATE ? 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#page-container .et_pb_section .et_pb_button_3:after,body #page-container .et_pb_section .et_pb_button_4:after,body #page-container .et_pb_section .et_pb_button_5:after{line-height:inherit;font-size:inherit!important;margin-left:-1em;left:auto;display:inline-block;opacity:0;content:attr(data-icon);font-family:ETmodules!important;font-weight:400!important}body #page-container .et_pb_section .et_pb_button_0:before,body #page-container .et_pb_section .et_pb_button_1:before,body #page-container .et_pb_section .et_pb_button_2:before,body #page-container .et_pb_section .et_pb_button_3:before,body #page-container .et_pb_section .et_pb_button_4:before,body #page-container .et_pb_section .et_pb_button_5:before{display:none}body #page-container .et_pb_section .et_pb_button_0:hover:after,body #page-container .et_pb_section .et_pb_button_1:hover:after,body #page-container .et_pb_section .et_pb_button_2:hover:after,body #page-container .et_pb_section .et_pb_button_3:hover:after,body #page-container .et_pb_section .et_pb_button_4:hover:after,body #page-container .et_pb_section .et_pb_button_5:hover:after{margin-left:.3em;left:auto;margin-left:.3em;opacity:1}}</style> <script> (function() { // Disable animations that cause CLS document.addEventListener('DOMContentLoaded', function() { // Find elements with animations that might cause CLS const animatedElements = document.querySelectorAll('.et-waypoint, .et_pb_animation_top, .et_pb_animation_bottom, .et_pb_animation_left, .et_pb_animation_right'); // Remove animation classes animatedElements.forEach(function(element) { element.classList.remove('et-waypoint', 'et_pb_animation_top', 'et_pb_animation_bottom', 'et_pb_animation_left', 'et_pb_animation_right'); element.style.opacity = '1'; element.style.transform = 'none'; }); // Fix for Divi sliders const sliders = document.querySelectorAll('.et_pb_slider'); sliders.forEach(function(slider) { // Set fixed height based on the first slide const firstSlide = slider.querySelector('.et_pb_slide'); if (firstSlide) { const slideHeight = firstSlide.offsetHeight; slider.style.height = slideHeight + 'px'; } }); // Fix for images without dimensions const images = document.querySelectorAll('img:not([width]):not([height])'); images.forEach(function(img) { if (img.naturalWidth && img.naturalHeight) { img.setAttribute('width', img.naturalWidth); img.setAttribute('height', img.naturalHeight); img.style.aspectRatio = img.naturalWidth + '/' + img.naturalHeight; } }); }); })(); </script>  <div id="cookie-notice" role="dialog" class="cookie-notice-hidden cookie-revoke-hidden cn-position-top" aria-label="Cookie Notice" style="background-color: rgba(50,50,58,1);"><div class="cookie-notice-container" style="color: #fff"><span id="cn-notice-text" class="cn-text-container">We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.</span><span id="cn-notice-buttons" class="cn-buttons-container"><a href="#" id="cn-accept-cookie" data-cookie-set="accept" class="cn-set-cookie cn-button cn-button-custom button" aria-label="Ok">Ok</a></span><span id="cn-close-notice" data-cookie-set="accept" class="cn-close-icon" title="No"></span></div> </div>  <span class="et_pb_scroll_top et-pb-icon"></span> </body> </html>