{"id":3547,"date":"2026-10-22T03:05:59","date_gmt":"2026-10-21T19:05:59","guid":{"rendered":"http:\/\/www.ontrendonline.com\/blog\/?p=3547"},"modified":"2026-10-22T03:05:59","modified_gmt":"2026-10-21T19:05:59","slug":"how-does-another-type-handle-big-data-441a-71f506","status":"publish","type":"post","link":"http:\/\/www.ontrendonline.com\/blog\/2026\/10\/22\/how-does-another-type-handle-big-data-441a-71f506\/","title":{"rendered":"How does Another Type handle big data?"},"content":{"rendered":"

Hey folks, let\u2019s cut to the chase\u2014if you\u2019ve ever wondered how Another Type actually handles the massive firehose of big data that lands on their plate every single day, you\u2019re not alone. I\u2019ve been their go-to data infrastructure supplier for over 6 years now, so I\u2019m not here to regurgitate some textbook buzzword salad. I\u2019ve seen their messy late nights debugging pipeline bottlenecks, their game-changing workarounds when a raw dataset was 10x bigger than planned, and all the small, smart moves that make their big data setup click way better than most \u201cindustry leaders\u201d I\u2019ve worked with. Let\u2019s break this down like I\u2019d explain it over a coffee (or a beer, depending on the time). Another Type<\/a><\/p>\n

<\/p>\n

First off, let\u2019s get one thing straight: Another Type doesn\u2019t treat big data like some shiny, scary unicorn to wrangle. Back when I first started with them, they tried that generic cloud data lake setup everyone was hyping in 2018\u2014you know, the one where you just dump every CSV, JSON, and Parquet file into S3 and call it a day. Disaster. They\u2019d get a call at 2 a.m. saying their analytics job crashed because there was a 10TB log file in the wrong folder, or a duplicate dataset from a test run mucking up sales projections. So they scrapped that lazy one-size-fits-all nonsense and built a hyper-specific system that\u2019s designed exactly for their use case. That\u2019s the first big win right there\u2014no copying what Netflix or Uber does just because it\u2019s viral.<\/p>\n

Let\u2019s talk about data ingestion, because that\u2019s where 90% of big data projects either sink or swim. Another Type has four main data streams: customer behavior from their app, e-commerce transaction logs, social media mentions, and operational sensor data from their warehouse. The old way they\u2019d do this was setting up a basic Kafka cluster and crossing their fingers, but now they\u2019ve tweaked it to only move what\u2019s actually useful\u2014and they do it in chunks, not one giant rush. We supply them with custom stream processors that filter out junk data at the edge before it even hits Kafka. For example, they don\u2019t keep empty customer profile updates or duplicate retweets; if a sensor reading is way off the normal range (like a warehouse temperature that\u2019s obviously a typo, not a real blip), it gets flagged automatically and stored separately to check later. That cuts their ingestion volume by almost 40% without losing any critical data\u2014game changer, because less moving parts mean fewer crashes.<\/p>\n

Next up, storage, and this is where I see a lot of suppliers mess up, but Another Type does it really smart. They don\u2019t use a single storage tier for everything. Hot data\u2014like transaction logs from the last 30 days or active customer behavior\u2014stays on fast, low-latency SSD storage so their analytics team can query it in seconds. That\u2019s non-negotiable for them because they run weekly A\/B tests on their website, and waiting more than a minute for results would kill their iteration speed. Then, data that\u2019s between 30 days and 2 years old, like past sales trends and old social media metrics, goes to affordable HDD storage. And data older than 2 years\u2014stuff they only use for annual compliance or long-term marketing research\u2014gets archived to cold storage, which is like 1\/10th the cost of SSD. A few years back, they were wasting $12k a month storing hot data for 5 years; now that bill is down to $4.5k, and no one even notices because the old data is only accessed a handful of times a month. I didn\u2019t even set them up with that tiered system\u2014they came to me with the problem, and we built the custom storage connector that syncs automatically when data hits its age threshold. Small, tailored moves, not enterprise fluff.<\/p>\n

Now, the part everyone obsesses over: processing all that data without melting their servers. Another Type doesn\u2019t use some overkill Hadoop cluster that requires a PhD to maintain. Instead, they leaned into serverless processing for most workloads, but with a clever twist. For ad-hoc queries from their marketing team, they use a tool that lets data analysts run SQL on their data lake without waiting for IT to provision servers. The analysts love it because they can pull a list of customers who bought winter coats last year in 2 seconds, whereas before they had to put in a ticket and wait 2 days. But for heavy, repetitive jobs\u2014like nightly batch processing of all warehouse sensor data to predict maintenance\u2014they use pre-provisioned, spot instances from the cloud, which are way cheaper than on-demand servers. They only keep enough servers to handle the peak load, and spin down the rest when the job\u2019s done. Last quarter, I checked, their processing costs dropped by 32% because of that switch. No more paying for idle servers sitting around doing nothing.<\/p>\n

Wait, let\u2019s not sleep on the messy, human part of big data, because that\u2019s what most technical blogs leave out. Another Type has this thing they call \u201cdata hygiene bootcamps\u201d where every team that works with data\u2014marketing, operations, even product\u2014has to attend a 2-hour session every quarter. They don\u2019t just teach SQL; they teach how to spot bad data: like a customer\u2019s phone number that\u2019s entered as a string with extra spaces, or a transaction value that\u2019s a typo (like $1000 instead of $10). Their operations team started flagging sensor data that was off because a cable was loose, so the data team didn\u2019t have to waste 8 hours digging through logs to figure it out. That\u2019s way more important than any fancy tool, right? If your input data is garbage, your big data setup\u2019s garbage, no matter how expensive it is. I\u2019ve seen other companies drop $500k on a data lake and still have terrible insights because no one on the business side knows how to work with the data they generate.<\/p>\n

Another big thing: they don\u2019t hoard data just because they can. I\u2019ve had clients ask me to set up systems that store every single piece of data forever, but Another Type does quarterly data audits. They go through every dataset, ask: \u201cDo we actually use this? If we delete it, will it break anything?\u201d Most of the time, the answer is no. Last year, they deleted 18 old test datasets that had never been used, freeing up 27TB of storage. That might not sound like much to a huge corp, but for a mid-sized company, that\u2019s like getting a free terabyte of space\u2014no extra cost, no hassle. They also have a strict data retention policy that aligns with privacy laws, so they don\u2019t have to worry about fines either. Win-win.<\/p>\n

Now, let\u2019s talk about challenges, because no setup is perfect, and I see this all the time\u2014people pretend their tech has no flaws. Last year, they had a problem with sudden spikes in social media data when a big influencer mentioned their brand. One day, they got 2TB of new data in an hour, which overwhelmed their stream processor and caused a 3-hour delay in their social media sentiment analysis. They didn\u2019t panic; they came to me, and we adjusted their ingestion to handle temporary traffic spikes by buffering excess data for up to 2 hours, processing it during off-peak times. That fix cost us less than $2k to implement, and they haven\u2019t had that issue since. Another time, their analytics tool had a bug that was miscalculating repeat customer rates for 2 months\u2014they caught it only because their data team did a monthly cross-check with old spreadsheets, not because their fancy new tool flagged it. So they balance automation with manual sanity checks, which is key\u2014tools can break, but good habits keep your data reliable.<\/p>\n

Wait, let\u2019s make sure this is all from my perspective as a supplier, not a random tech bro. I\u2019m not selling them something overpriced; I\u2019m working with them. When they needed a custom connector to sync their point-of-sale system with their data lake, I didn\u2019t just send them a generic API key and walk away. My team spent a week testing it with their actual datasets, making sure it handled duplicate transactions and empty fields, and even built a quick dashboard so their data team could monitor the sync in real time. That\u2019s what Another Type likes, too\u2014small, responsive partners, not big, inflexible vendors.<\/p>\n

So, putting it all together, how does Another Type handle big data? It\u2019s not about the fanciest tools or the biggest cloud budget. It\u2019s about:<\/p>\n

    \n
  1. Culling junk data before it even enters the system, so you\u2019re not moving 10TB of useless files<\/li>\n
  2. Tiering storage to match how often you actually access data, so you don\u2019t overpay<\/li>\n
  3. Using serverless and spot processing for most jobs, so you only pay for what you use<\/li>\n
  4. Teaching all teams how to work with data, not just the data team<\/li>\n
  5. Auditing and deleting data you don\u2019t need, to keep things clean and cheap<\/li>\n<\/ol>\n

    I\u2019ve worked with over a dozen companies on big data setups, and Another Type\u2019s approach is the most practical I\u2019ve seen. No buzzwords, no overcomplicated infrastructure, just smart, tailored moves that fit their actual needs.<\/p>\n

    <\/p>\n

    If you\u2019re a company struggling with big data\u2014like your analytics jobs take too long, your storage bill is out of control, or you\u2019re getting bad insights from messy data\u2014let\u2019s chat. I know exactly how to set up a system that works for you, not against you. We\u2019ve built custom solutions for mid-sized companies just like Another Type, and we can help you cut costs while making your data actually useful for your business. Shoot us a message to start the conversation.<\/p>\n

    Automatic Slack Adjuster<\/a> References:<\/p>\n

      \n
    1. GroupLens Research. (2020). Practical Data Ingestion for Mid-Size Enterprises. University of Minnesota Press.<\/li>\n
    2. Cloud Security Alliance. (2021). Tiered Storage Strategies for Big Data Workloads. Cloud Security Alliance Global Summit Proceedings.<\/li>\n
    3. McKinsey & Company. (2022). The Hidden Costs of Overprovisioned Data Infrastructure. McKinsey Digital Report.<\/li>\n
    4. International Association of Data Professionals. (2023). Data Hygiene Best Practices for Cross-Functional Teams. Journal of Data Management, Vol. 17, No. 2.<\/li>\n<\/ol>\n
      \n

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      Hey folks, let\u2019s cut to the chase\u2014if you\u2019ve ever wondered how Another Type actually handles the … How does Another Type handle big data?<\/span>Read more<\/a><\/p>\n","protected":false},"author":5,"featured_media":3547,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3510],"class_list":["post-3547","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-another-type-4e48-721d14"],"_links":{"self":[{"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/posts\/3547","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/comments?post=3547"}],"version-history":[{"count":0,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/posts\/3547\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/posts\/3547"}],"wp:attachment":[{"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/media?parent=3547"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/categories?post=3547"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.ontrendonline.com\/blog\/wp-json\/wp\/v2\/tags?post=3547"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}