{"id":10463,"date":"2021-12-16T17:13:23","date_gmt":"2021-12-16T17:13:23","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=10463"},"modified":"2024-03-28T13:48:32","modified_gmt":"2024-03-28T13:48:32","slug":"are-your-microservices-hiding-data-products","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/are-your-microservices-hiding-data-products\/","title":{"rendered":"Are your microservices hiding data products?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">We&#8217;ve worked with many customers to help them create microservice architectures. One pattern we\u2019ve observed is that microservices sometimes take on a data engineering capability. When this happens, there\u2019s a hidden data product, waiting to come to the fore. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">With an established team already responsible for collecting and understanding the data, it can be a great starting point for an organisation new to data-driven decision making, and greatly empower capabilities such as Machine Learning.<\/span><\/p>\n<h3><b>What is a data product?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Just like a microservice, a data product is a domain-bounded, isolated product capability that\u00a0 provides value to its users. Unlike a microservice, the users of a data product interact with it in an ad-hoc manner, and there\u2019s no specific set of user interactions.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A data product may be:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Surfaced through a business intelligence tool, for user generated reporting.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Combined by data scientists with other data products, to enable Machine Learning.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Brought into an operational data store, for real time usage by a microservice.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Leveraged by data engineers in a data pipeline to create new data products.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By extracting your data product, the owning team of the microservice can take responsibility for it and ensure it has the data engineering practices, data quality guarantees, and product ownership it needs. If a data product remains concealed within a microservice, users will struggle to leverage the data in a way that empowers the organisation.<\/span><\/p>\n<h3><b>Signs you\u2019ve got a hidden data product<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The following are some weak indicators of a hidden data product:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Users keep asking for more reports.<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Direct requests for the data<\/b><span style=\"font-weight: 400;\">.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>You can no longer process all the data in the application.<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It\u2019s a strong indicator if your microservice data is being used in a <\/span><a href=\"https:\/\/playbooks.equalexperts.com\/data-pipeline\"><span style=\"font-weight: 400;\">data pipeline<\/span><\/a><span style=\"font-weight: 400;\">. If it&#8217;s not being treated as a data product, it is probably fragile, lacking productionisation, and its development is being driven by microservice needs and not by other users of the data. This can look like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Exports or replications of the data.<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Users ask for more data sources to be added to enrich the data.<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>You spend most application development time handling data transformation.<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">One of these signals may not be cause enough for splitting out a data product, but a combination of them builds a very strong case!\u00a0<\/span><\/p>\n<h3><b>Example: Fraud case working application<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A fintech company needs a way to protect and investigate potential fraudulent usages of its platform. To do so, they have created a case working tool for their fraud analysts that provides a summarised view across incoming transactions for quick assessment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This data transformation logic takes place within the microservice, and is sourced from the raw unstructured data store.<\/span><\/p>\n<img decoding=\"async\" class=\"alignnone size-large wp-image-10464\" src=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2021\/12\/Microservices-data-content-image-1-1200x483.jpg\" alt=\"\" width=\"1200\" height=\"483\" \/>\n<p><span style=\"font-weight: 400;\">The application naturally grew in size over time and several of our data product indicators are now present:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data transformations inside the application constitute most of the engineering work, with users frequently asking for it to be extended.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The RDS store of summarised result data has been seen as very valuable by data scientists, and easier to work with than the raw semi-structured data, and a replica has been shared.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The RDS replica is now also used as the basis for a machine learning pipeline.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">These signals all point to the data inside the case working application as being exceptionally valuable, and worthy of being a data product.<\/span><\/p>\n<h3><b>How to surface your data product\u00a0<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Start out by building a data pipeline for your data product, assuming it doesn\u2019t exist already. We\u2019ve got a <\/span><a href=\"https:\/\/playbooks.equalexperts.com\/data-pipeline\"><span style=\"font-weight: 400;\">Data Pipeline Playbook<\/span><\/a><span style=\"font-weight: 400;\"> that can help you with this.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Your team may need to bring in some data engineering expertise, but the developers who own the relevant microservice will be able to pair with them, and promote a cross-discipline approach to implementing your new data product.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Aim for a steel thread implementation, and try surfacing it directly to analytical users for feedback. After that, start to integrate it back into your microservice, and replace the microservice code. This is an example of the popular <\/span><a href=\"https:\/\/martinfowler.com\/bliki\/StranglerFigApplication.html\"><span style=\"font-weight: 400;\">Strangler Fig pattern<\/span><\/a><span style=\"font-weight: 400;\"> for incremental design.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s important to remember that your microservice is no longer the sole driver of this data product, and that it may need to do its own transformation of the data, so as not to disrupt other consumers of your new data product.<\/span><\/p>\n<h3><b>Example: Fraud case working application<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Back to the project team at our fintech. Now aware of the value of the summarised data in the case working app, they have taken the steps of separating it into its own data pipeline, running on the AWS EMR\/Spark stack.<\/span><\/p>\n<img decoding=\"async\" class=\"alignnone size-large wp-image-10465\" src=\"https:\/\/www.equalexperts.com\/wp-content\/uploads\/2021\/12\/Microservices-data-content-image-2-1200x418.jpg\" alt=\"\" width=\"1200\" height=\"418\" \/>\n<p><span style=\"font-weight: 400;\">This has provided multiple benefits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The footprint of the application is significantly reduced.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The data product is now leveraged not only by data scientists, but also other users such as performance analysts, giving them a cleaner data set to work with.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Other data products have begun to leverage the summarised data set beyond initial plans, in this case a growing product for analytical insights and fraud alerts.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data enrichment, data quality, validation tests, and versioning management can all be managed around the data product itself.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">They can now process much more data and extend its historical accuracy by moving it to a purpose built tool in Spark.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">A lot of inspiration and central concepts for this blog come from the <\/span><a href=\"https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html\"><span style=\"font-weight: 400;\">Data Mesh Architecture<\/span><\/a><span style=\"font-weight: 400;\"> proposed by Zhamek Dhegani.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We wish your new Data Product the best of luck on its journey!<\/span><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>How finding hidden data products can greatly empower capabilities such as Machine Learning within an organisation<\/p>\n","protected":false},"author":167,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[188,230],"location":[397],"class_list":["post-10463","post","type-post","status-publish","format-standard","hentry","category-tech-focus","tag-data-engineering","tag-microservices"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Are your microservices hiding data products? | Equal Experts<\/title>\n<meta name=\"description\" content=\"Microservices sometimes take on a data engineering capability. 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