{"id":3508,"date":"2022-10-17T00:35:24","date_gmt":"2022-10-17T00:35:24","guid":{"rendered":"http:\/\/localhost\/daltix\/?p=3508"},"modified":"2022-10-26T13:46:58","modified_gmt":"2022-10-26T13:46:58","slug":"data-access","status":"publish","type":"post","link":"https:\/\/www.daltix.com\/en\/news\/data-access\/","title":{"rendered":"Daltix Data Architecture: Data Access"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3508\" class=\"elementor elementor-3508\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<header class=\"elementor-section elementor-top-section elementor-element elementor-element-41baa53f elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"41baa53f\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7cacf415\" data-id=\"7cacf415\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7bb57664 elementor-widget elementor-widget-heading\" data-id=\"7bb57664\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Daltix Data Architecture: Data Access\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/header>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b35d512 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b35d512\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ef072a2\" data-id=\"ef072a2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d5a551 elementor-widget elementor-widget-text-editor\" data-id=\"6d5a551\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>26 October 2022<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93e5b5a elementor-widget elementor-widget-text-editor\" data-id=\"93e5b5a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong>If you missed the three previous posts in the series, here are links to the last three:<\/strong><\/p><ul><li><strong><a href=\"https:\/\/www.daltix.com\/en\/resources\/news\/introducing-the-daltix-data-architecture\/\">Introducing the Daltix Data Architecture<\/a><\/strong><\/li><li><strong><a href=\"https:\/\/www.daltix.com\/en\/resources\/news\/data-extraction\/\">Data Extraction<\/a><\/strong><\/li><li><a href=\"https:\/\/www.daltix.com\/en\/resources\/news\/data-standardisation-and-shaping\/\"><strong>Data Standardisation and Shaping\u00a0<\/strong><\/a><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0db2bea elementor-widget elementor-widget-image\" data-id=\"0db2bea\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"319\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/1st-picture.png\" class=\"attachment-large size-large wp-image-15872\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/1st-picture.png 904w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/1st-picture-300x119.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/1st-picture-768x306.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e742e66 elementor-widget elementor-widget-text-editor\" data-id=\"e742e66\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">So far in this Data Architecture series, we\u2019ve explained how our data gets transformed from its raw format (HTML pages or JSON documents dumped on S3) to a queryable structured data set available to analysts in Snowflake. In this post we\u2019ll discuss the final piece of the puzzle &#8211; and often the hardest one: how do we ensure that everyone who needs to access our data, can do so in a way that fits their use case? To answer this, we\u2019ll first take a look at two popular data setups: the data lake and the data warehouse. We\u2019ll discuss our experiences with each, and how they fit in our data architecture.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2028265 elementor-widget elementor-widget-heading\" data-id=\"2028265\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">On lakes and warehouses<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76247f6 elementor-widget elementor-widget-text-editor\" data-id=\"76247f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Data lakes and warehouses are two ways of managing data and getting value out of it, but they serve different use cases. The older of the two, the data warehouse, came to the fore back when transactional relational databases made up the vast majority of data systems in companies. As data analysis became more prevalent, people realised that the analytical queries they were running were very different from the typical transactional query: instead of looking up a single row and updating a value, you were now aggregating huge data sets and calculating metrics over them.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e95909 elementor-widget elementor-widget-image\" data-id=\"5e95909\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"384\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.05.44-1024x491.png\" class=\"attachment-large size-large wp-image-16049\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.05.44-1024x491.png 1024w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.05.44-300x144.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.05.44-768x368.png 768w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.05.44.png 1340w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ddba498 elementor-widget elementor-widget-text-editor\" data-id=\"ddba498\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Transactional systems weren\u2019t built for this type of queries, and neither was the often heavily normalised structure that data was stored in. This is where data warehouses came in: they were created to facilitate aggregations and occasional single-row lookups, rather than many random access line updates. Along with the technology to allow this, new ways of modelling data to make analytical queries faster to run and easier to write were also adopted, moving away from the normalised structures of transactional systems.<\/span><\/p><p><span style=\"font-weight: 400;\">While data warehouses allow much more efficient analytical processing, they are still mostly relational systems. This means that, by and large, data stored must follow a certain format. There are ways to mitigate this: many warehouse platforms allow you to store free text or formats like JSON and XML in columns, but often these capabilities are limited and queries on them are not as performant on other columns. Often, fitting in data that did not adhere to a pre-defined, fixed schema meant either rewriting your schema (and transforming all your data already in it), or just not including that data at all. With unstructured data, e.g. images, video, IoT data etc., being seen more and more as valuable, this posed a challenge. Enter the data lake.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cbcde35 elementor-widget elementor-widget-image\" data-id=\"cbcde35\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"383\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.07.36-1024x490.png\" class=\"attachment-large size-large wp-image-16052\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.07.36-1024x490.png 1024w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.07.36-300x144.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.07.36-768x368.png 768w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.07.36.png 1232w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e73c74f elementor-widget elementor-widget-text-editor\" data-id=\"e73c74f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">At its core, the data lake seeks to allow storing all your data, structured and unstructured, in one place cheaply, and to let you access it there directly. At first, it was really a storage solution: queries directly on the data lake were slow, and not expected to rival the speed of data warehouse queries. ETL pipelines read their data from the data lake into a processing engine, and stored it in data warehouses for routine analysis, but exploratory analysis could happen directly on data in the lake. More recently, tools have become available that improve data lake query performance. Some, like Databricks with Delta Lake, argue that this query performance has now caught up with data warehouse performance, and there should no longer be a distinction between data lake and warehouse, merging the two into the data lakehouse.<\/span><\/p><p><span style=\"font-weight: 400;\">Since data lakes are meant primarily for storing data, they often take a hands-off approach to correcting data in it. It is often a cumbersome task: downloading a file, finding the line that needs updating, updating it, then uploading the entire file that contained it again to replace the original. For some use cases correcting data is not really an issue: when you\u2019re collecting IoT sensor data for example, anomalies and wrong readings are expected, and consumers of this type of data typically handle this already. For other use cases, like ours, having wrong data in our lake means there is a risk we\u2019ll someday sell that data to our customers. The data warehouse handles this more gracefully: you can usually update single records easily with an update command. Again, Delta Lake blurs the lines between the two technologies by allowing easy record-level updates in your lake directly.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed4e42d elementor-widget elementor-widget-image\" data-id=\"ed4e42d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"658\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.01.06-1024x842.png\" class=\"attachment-large size-large wp-image-16046\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.01.06-1024x842.png 1024w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.01.06-300x247.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.01.06-768x631.png 768w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/Screenshot-2022-10-24-at-16.01.06.png 1226w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9839198 elementor-widget elementor-widget-heading\" data-id=\"9839198\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Our data lake and warehouse<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c5c35d elementor-widget elementor-widget-image\" data-id=\"0c5c35d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"260\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/3rd-picture.png\" class=\"attachment-large size-large wp-image-15875\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/3rd-picture.png 904w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/3rd-picture-300x98.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/3rd-picture-768x250.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5a9f2d elementor-widget elementor-widget-text-editor\" data-id=\"a5a9f2d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">We introduced a data lake &#8211; comprising the raw, extracted, and standardised layers we discussed in previous posts &#8211; into our architecture exactly for the reason that it was originally developed: to give us more freedom in storing data in different formats, including HTML and JSON. This meant we no longer had to try and fit data to a schema that was pre-defined. Running batch transformation jobs (using Dask) that read their input from the lake, and wrote back to it, also worked marvellously.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Before long though, we found that this schema-on-read approach was putting a big burden on our data analysts. It often meant they were required to write queries to generate intermediate tables, rather than having the data stored in the required format ready for use. Also things like updating data for reporting (e.g. to correct a previous incorrect value) resulted in complex queries that needed to be written. In a way we had made our data very accessible to data engineers, but not really for our analytics teams who make the data go the last mile to the customer.<\/span><\/p><p><span style=\"font-weight: 400;\">This was exactly the scenario that data warehouses excelled in though, so we added Snowflake to our infrastructure to transform and store our ready-to-use data. This meant that we could easily set up ETL pipelines written in SQL to transform the data into tables that were friendly to use for our analysts. This gain in efficiency for analysis was well worth the higher price for storing data in Snowflake rather than S3.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3561cb elementor-widget elementor-widget-heading\" data-id=\"f3561cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Bespoke access layer<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-019f58b elementor-widget elementor-widget-image\" data-id=\"019f58b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"732\" height=\"770\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/4th-picture.png\" class=\"attachment-large size-large wp-image-15977\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/4th-picture.png 732w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/4th-picture-285x300.png 285w\" sizes=\"(max-width: 732px) 100vw, 732px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b19bfbe elementor-widget elementor-widget-text-editor\" data-id=\"b19bfbe\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Once the data is in Snowflake in its final format, our standardised customer dashboards, written in Looker, read it directly from there. When we ship data to the customer, this also happens by extracting data directly from Snowflake to send on to them. However, we found that for some use cases that go beyond analytics, Snowflake did not provide the performance we needed. For these cases we\u2019ve introduced a final stage in our data architecture: the bespoke access layer.<\/span><\/p><p><span style=\"font-weight: 400;\">For our web applications such as our product matching tool we make a lot of single data point lookups, and we found that Snowflake struggled to retrieve these with low enough latency to not slow down the UI: while a second is not long to wait while doing some data analysis, it is very long when you\u2019re waiting for your live dashboard to respond! To solve this, we introduced an AWS Aurora instance. Snowflake still serves up the necessary data here, and the subset of data required is merely replicated to AWS Aurora; the web application then reads it from there.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Our web application also includes free-text search boxes. Again, we found that Snowflake was not the best tool for the job here, but OpenSearch gave us the best performance for this type of queries, so we also replicate the data we need for this from Snowflake to OpenSearch.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eabcebf elementor-widget elementor-widget-image\" data-id=\"eabcebf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"315\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/5th-picture.png\" class=\"attachment-large size-large wp-image-15980\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/5th-picture.png 904w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/5th-picture-300x118.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/10\/5th-picture-768x302.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af4ee82 elementor-widget elementor-widget-text-editor\" data-id=\"af4ee82\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">That brings us to the end of the last post in this series where we\u2019ve traced our data flow from ingestion into the data lake, over transformation through AWS Lambdas, Dask, and Snowflake, to presentation in the data warehouse in Snowflake and the bespoke access layer on Amazon Aurora and Opensearch. As is probably clear from this architecture, we strongly believe in using the right tool for the job, not tailoring the job to the tools you have. The combination of a data lake with a data warehouse allows us to keep huge amounts of data without limiting either the performance of the queries we run today, or which queries we can run in the future.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">This architecture allows us to provide our customers with the data they need at a quality they\u2019ve come to expect. In the world of data though, things are always moving, and we\u2019re always considering new technologies or best practices to solve challenges we still have (like making our ETLs more testable and making sure our data lake also contains no incorrect values). It\u2019s a journey, and we\u2019re always welcoming talent to join us on it!<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-2160c41 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2160c41\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-adb7be5\" data-id=\"adb7be5\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-506c39b text-grad elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"506c39b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">Share this article<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-037775f\" data-id=\"037775f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-694c307 elementor-share-buttons--view-icon elementor-share-buttons--shape-circle elementor-share-buttons--color-custom elementor-share-buttons--skin-minimal elementor-grid-0 elementor-widget elementor-widget-share-buttons\" data-id=\"694c307\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"share-buttons.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-grid\" role=\"list\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_facebook\" role=\"button\" tabindex=\"0\" aria-label=\"Share on facebook\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-facebook\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_linkedin\" role=\"button\" tabindex=\"0\" aria-label=\"Share on linkedin\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-linkedin\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t\t<div class=\"elementor-share-btn elementor-share-btn_pinterest\" role=\"button\" tabindex=\"0\" aria-label=\"Share on pinterest\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-share-btn__icon\">\n\t\t\t\t\t\t\t\t<i class=\"fab fa-pinterest\" aria-hidden=\"true\"><\/i>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-408d0c5 elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"408d0c5\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-19cd146\" data-id=\"19cd146\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-007072b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"007072b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-e6c65df\" data-id=\"e6c65df\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-06dcadc elementor-widget elementor-widget-heading\" data-id=\"06dcadc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"title-border2\">Related<\/span> resources.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-7197df2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7197df2\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-16344ac my-post\" data-id=\"16344ac\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-22983e8 elementor-posts__hover-none elementor-grid-tablet-3 elementor-grid-3 elementor-grid-mobile-1 elementor-posts--thumbnail-top elementor-widget elementor-widget-posts\" data-id=\"22983e8\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;cards_columns_tablet&quot;:&quot;3&quot;,&quot;cards_columns&quot;:&quot;3&quot;,&quot;cards_columns_mobile&quot;:&quot;1&quot;,&quot;cards_row_gap&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:35,&quot;sizes&quot;:[]},&quot;cards_row_gap_laptop&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;cards_row_gap_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;cards_row_gap_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"posts.cards\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-posts-container elementor-posts elementor-posts--skin-cards elementor-grid\" role=\"list\">\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-19031 post type-post status-publish format-standard has-post-thumbnail hentry category-news\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/www.daltix.com\/en\/news\/supermarkets-vs-discounters-germany\/\" tabindex=\"-1\" ><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"1707\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-scaled.jpg\" class=\"attachment-full size-full wp-image-13071\" alt=\"Happy woman shopping for frozen food with smart phone.\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-scaled.jpg 2560w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-300x200.jpg 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-1024x683.jpg 1024w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-768x512.jpg 768w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-1536x1024.jpg 1536w, https:\/\/www.daltix.com\/wp-content\/uploads\/2022\/05\/iStock-538863826-2048x1365.jpg 2048w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">News<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/www.daltix.com\/en\/news\/supermarkets-vs-discounters-germany\/\" >\n\t\t\t\tDecoding the German retail landscape: Supermarkets vs Discounters \ud83d\udcf0\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>To make smart decisions, retailers need high-quality and reliable retail data. Those who have access to price, product and promotion data on a daily basis have a clear advantage. Daltix and Smartspotter are partnering and can now deliver both of their data expertise of high-quality data to Germany.<\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/www.daltix.com\/en\/news\/supermarkets-vs-discounters-germany\/\" aria-label=\"Read more about Decoding the German retail landscape: Supermarkets vs Discounters \ud83d\udcf0\" tabindex=\"-1\" >\n\t\t\tRead article\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-18528 post type-post status-publish format-standard has-post-thumbnail hentry category-news\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/www.daltix.com\/en\/news\/how-retailers-can-use-data-efficiently\/\" tabindex=\"-1\" ><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"1200\" height=\"1200\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter.png\" class=\"attachment-full size-full wp-image-18511\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter.png 1200w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter-300x300.png 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter-1024x1024.png 1024w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter-150x150.png 150w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/04\/Daltix-and-Smartspotter-768x768.png 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">News<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/www.daltix.com\/en\/news\/how-retailers-can-use-data-efficiently\/\" >\n\t\t\t\t35 million data points: Retailers and suppliers can now use on- and offline data more efficiently\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>To make smart decisions, retailers need high-quality and reliable retail data. Those who have access to price, product and promotion data on a daily basis have a clear advantage. Daltix and Smartspotter are partnering and can now deliver both of their data expertise of high-quality data to Germany.<\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/www.daltix.com\/en\/news\/how-retailers-can-use-data-efficiently\/\" aria-label=\"Read more about 35 million data points: Retailers and suppliers can now use on- and offline data more efficiently\" tabindex=\"-1\" >\n\t\t\tRead article\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-18178 post type-post status-publish format-standard has-post-thumbnail hentry category-news\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/www.daltix.com\/en\/news\/ceo-daltix\/\" tabindex=\"-1\" ><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"800\" src=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/03\/Dries-.jpeg\" class=\"attachment-full size-full wp-image-18201\" alt=\"\" srcset=\"https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/03\/Dries-.jpeg 800w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/03\/Dries--300x300.jpeg 300w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/03\/Dries--150x150.jpeg 150w, https:\/\/www.daltix.com\/wp-content\/uploads\/2023\/03\/Dries--768x768.jpeg 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">News<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/www.daltix.com\/en\/news\/ceo-daltix\/\" >\n\t\t\t\tRenewed focus brings on new CEO at Daltix\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>As Daltix expands into Europe, it renews its focus on developing a unique product with its customers at the centre. This change has brought a new CEO to Daltix.  <\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/www.daltix.com\/en\/news\/ceo-daltix\/\" aria-label=\"Read more about Renewed focus brings on new CEO at Daltix\" tabindex=\"-1\" >\n\t\t\tRead article\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<\/div>\n\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-879fb76 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"879fb76\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-50ecffe\" data-id=\"50ecffe\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5070c90 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"5070c90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-size-sm\" role=\"button\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">See all resources<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-70a10ee elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-id=\"70a10ee\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6c94029\" data-id=\"6c94029\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-8d67312 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8d67312\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-75a6db2\" data-id=\"75a6db2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0de03be elementor-widget elementor-widget-heading\" data-id=\"0de03be\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">This is what <span class=\"title-border2\">Daltix<\/span><br \/>stands for.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-5ed9e0f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5ed9e0f\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-8f1800d\" data-id=\"8f1800d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cf2c003 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"cf2c003\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<div class=\"elementor-icon\">\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"80\" 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elementor-element-aabb13d elementor-widget elementor-widget-heading\" data-id=\"aabb13d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Integrity is our superpower.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-f27378b\" data-id=\"f27378b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5109df4 elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"5109df4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div 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d=\"M38.592 62.5478H57.0957V53.0972H38.592V62.5478Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M20.1339 62.5478H38.6377V53.0972H20.1339V62.5478Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M1.65245 62.5478H20.1562V53.0972H1.65245V62.5478Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M38.592 43.647H57.0957V34.1964H38.592V43.647Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M20.1329 43.647H38.6367V34.1964H20.1329V43.647Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M1.65245 43.647H20.1562V34.1964H1.65245V43.647Z\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><\/mask><g mask=\"url(#mask0_222_3236)\"><rect 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29.9161 48.9845 29.5956 47.632 29.0229C47.3842 28.9192 47.1146 28.8782 46.8472 28.9035C46.5798 28.9288 46.3227 29.0196 46.0987 29.1679C45.8747 29.3162 45.6908 29.5175 45.5631 29.7538C45.4354 29.9902 45.368 30.2544 45.3667 30.523V45.874H37.989\" stroke=\"#37B4A2\" stroke-width=\"2.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><\/mask><g mask=\"url(#mask0_222_3243)\"><rect width=\"95\" height=\"73\" fill=\"url(#paint0_linear_222_3243)\"><\/rect><\/g><defs><linearGradient id=\"paint0_linear_222_3243\" x1=\"7.66651\" y1=\"36.5\" x2=\"105.897\" y2=\"36.5\" gradientUnits=\"userSpaceOnUse\"><stop stop-color=\"#0FB1A2\"><\/stop><stop offset=\"1\" stop-color=\"#524488\"><\/stop><\/linearGradient><\/defs><\/svg>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-772c5ab elementor-widget elementor-widget-heading\" data-id=\"772c5ab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Collaboration is key.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-fc467c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc467c0\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-245fcfe\" data-id=\"245fcfe\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4da247e 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To answer that, we&#8217;ll examine our two data setups and explain our experiences and how they fit into our data architecture.<\/p>\n","protected":false},"author":7,"featured_media":15974,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[13,84],"tags":[],"class_list":["post-3508","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-tech"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Daltix Data Architecture: Data Access - Daltix<\/title>\n<meta name=\"description\" content=\"In the final blog post of the data architecture series, we\u2019ll take a look at how Daltix can provide everyone who requires their data access in a way that suits them. 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