Close
  • Latest News
  • Cybersecurity
  • Big Data and Analytics
  • Cloud
  • Mobile
  • Networking
  • Storage
  • Applications
  • IT Management
  • Small Business
  • Development
  • Database
  • Servers
  • Android
  • Apple
  • Innovation
  • Blogs
  • PC Hardware
  • Reviews
  • Search Engines
  • Virtualization
Read Down
Sign in
Close
Welcome!Log into your account
Forgot your password?
Read Down
Password recovery
Recover your password
Close
Search
Menu
Search
  • Latest News
  • Cybersecurity
  • Big Data and Analytics
  • Cloud
  • Mobile
  • Networking
  • Storage
  • Applications
  • IT Management
  • Small Business
  • Development
  • Database
  • Servers
  • Android
  • Apple
  • Innovation
  • Blogs
  • PC Hardware
  • Reviews
  • Search Engines
  • Virtualization
More
    Home Latest News
    • Storage

    The Sushi Principle: Why Data Is Better Raw Than ‘Cooked’

    By
    Chris Preimesberger
    -
    October 6, 2015
    Share
    Facebook
    Twitter
    Linkedin

      PrevNext

      1The Sushi Principle: Why Data Is Better Raw Than ‘Cooked’

      1 - The Sushi Principle: Why Data Is Better Raw Than 'Cooked'

      Organizations often need to use data in a different way than originally planned. For flexibility and accuracy, they need fast access to data in its raw state.

      2How Systems Are Currently Set Up

      2 - How Systems Are Currently Set Up

      Most data systems are made up of three systems: ETL (extract, transform and load) system, databases and a business intelligence layer. Here is how most companies build IT systems: They buy the three systems noted above, hire a database architect, a database administrator, a project manager and a dozen engineers. They then decide on data schema(s) and wait months while the engineers put everything together. Finally, they spend years finding bugs and fixing errors. Efficient? Not very.

      3Where Data Comes From and Where It’s Stored

      3 - Where Data Comes From and Where It's Stored

      Raw data comes from servers, production databases, the Internet of things sensors and devices, and other periodic measuring or reporting devices. Most businesses store this data in data warehouses, data marts or so-called data lakes (a huge central pool of data). The classic data warehouse splits data into facts about the world, dimensions that describe the facts, and aggregates or metadata that summarize the facts.

      4Data Warehouses Contain ‘Cooked’ Data

      4 - Data Warehouses Contain 'Cooked' Data

      Before they store data, businesses “cook” it by cleaning, compressing, de-duplicating, normalizing, filtering and summarizing it, all in the name of optimizing access to the data. Many businesses move normalized information from fact tables to dimension table indexes; they build bitmap and B-tree indices—often many per table. This leads to pre-computed aggregates and building of online analytical processing (OLAP) cubes, materialized views and other aggregates.

      5So Why Do We Cook Data?

      5 - So Why Do We Cook Data?

      Most cooking of data is actually an application-level optimization. Normalizing data, building indexes and pre-computing aggregates make data warehouses fast and compact, but they also make data warehouses error-prone, difficult to maintain and difficult to understand. With today’s distributed architectures and faster processors, it’s now more efficient—and accurate—to pull information from raw data than it is to depend on summary tables that pull from many different systems and apply obscure logic.

      6The Sushi Principle, Step 1: Don’t Pre-Process

      6 - The Sushi Principle, Step 1: Don't Pre-Process

      Don’t pre-process data when you pull it in, store it or query it. Use a well-tested pipeline and keep it simple. Resist the temptation to add business logic, or you’ll end up with concerns about accuracy, won’t be able to get back to the original data source, and are likely to break the pipeline and potentially lose data.

      7The Sushi Principle, Step 2: Store All Data

      7 - The Sushi Principle, Step 2: Store All Data

      Store all your data with all its raw details, but partitioned and sorted for fast analysis. They can be in different silos, but a centralized management system is a must.

      8The Sushi Principle, Step 3: Summarize and Sample

      8 - The Sushi Principle, Step 3: Summarize and Sample

      Summarize and sample at query time for direct access to complete, accurate and fresh data. This guarantees that you will have the most up-to-date information possible.

      9Use Cases: Facebook and LinkedIn Were Early Adopters

      9 - Use Cases: Facebook and LinkedIn Were Early Adopters

      At first, these two social networking giants built data lakes before figuring out how to make the data ready to use. They were among the first companies to realize that raw data had more potential value than data that had been cooked. They pulled in data with as little pre-processing as possible into data lakes. They then developed processes that analyzed raw data in response to queries, delivering fresh, customized, “ready-to-eat” information instead of just pulling up pre-built, black-box summaries and aggregates.

      PrevNext

      MOST POPULAR ARTICLES

      Android

      Samsung Galaxy XCover Pro: Durability for Tough...

      Chris Preimesberger - December 5, 2020 0
      Have you ever dropped your phone, winced and felt the pain as it hit the sidewalk? Either the screen splintered like a windshield being...
      Read more
      Cloud

      Why Data Security Will Face Even Harsher...

      Chris Preimesberger - December 1, 2020 0
      Who would know more about details of the hacking process than an actual former career hacker? And who wants to understand all they can...
      Read more
      Cybersecurity

      How Veritas Is Shining a Light Into...

      eWEEK EDITORS - September 25, 2020 0
      Protecting data has always been one of the most important tasks in all of IT, yet as more companies become data companies at the...
      Read more
      Big Data and Analytics

      How NVIDIA A100 Station Brings Data Center...

      Zeus Kerravala - November 18, 2020 0
      There’s little debate that graphics processor unit manufacturer NVIDIA is the de facto standard when it comes to providing silicon to power machine learning...
      Read more
      Apple

      Why iPhone 12 Pro Makes Sense for...

      Wayne Rash - November 26, 2020 0
      If you’ve been watching the Apple commercials for the past three weeks, you already know what the company thinks will happen if you buy...
      Read more
      eWeek


      Contact Us | About | Sitemap

      Facebook
      Linkedin
      RSS
      Twitter
      Youtube

      Property of TechnologyAdvice.
      Terms of Service | Privacy Notice | Advertise | California - Do Not Sell My Information

      © 2021 TechnologyAdvice. All Rights Reserved

      Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.

      ×