Types of Data Mining Architecture: A Complete Guide for Beginners

Welcome lone reader! If you find data architecture complicated, you‘re not alone. With the dizzying array of terms like data warehouses, lakes, and coupling styles, it‘s enough to make any technologist‘s head spin!

Fear not – this step-by-step guide aims to explain everything you need to know clearly, starting with what data mining architecture actually entails…

What is Data Mining Architecture Anyway?

Data mining architecture refers to the technical blueprint for building pipelines to collect, store, and analyze data. Just like how architectural plans are vital for constructing stable buildings, data architecture ensures the structural soundness of systems handling data at scale.

For example, let‘s examine the typical architecture powering analytics at tech juggernauts like Google, Meta or Uber:

Sample Data Architecture

A typical robust modern data stack (Source: interesting-facts.com)

As you can see, the tech giants ingest data from countless sources like mobile apps, websites, IoT sensors into cloud storage like data lakes. Then the structured data is cleaned and processed to fill data warehouses and marts that power business intelligence dashboards as well as custom machine learning workflows.

While stacks differ across industries and maturity levels, they all share a common blueprint – the data mining architecture guiding infrastructure, storage, and access.

Now let‘s peel back the layers starting from raw sources to insights…

Journey of Data: From Sources to Discovery

Like precious ore, data goes through refining stages before delivering value. The key phases include:

Stage 1: Sources – Where raw data gets generated from systems like databases, logs, streams etc. Think of this as the point minerals get extracted from mines!

Stage 2: Staging & Processing – The intermediary phase where data gets transformed and enriched through ETL pipelines into analysis-ready form before storage. Similar to how raw ore gets smelted and refined.

Stage 3: Storage & Management – Where processed data lands – into ‘warehouses‘, ‘lakes‘ etc. – for persistence, governance and access. Like how gold gets stored in secure vaults.

Stage 4: Consumption & Analysis – Where data reaches downstream users – in reports, dashboards, ML models etc. – leading to intelligence. Just as how gold, once stored, gets shaped into jewelry and other artifacts creating economic value.

We‘ll dig deeper into the components soon. But first, let‘s shine the spotlight on a crucial backbone tying it all together – the data architecture style itself.

4 Major Data Mining Architecture Types

Like building architecture styles ranging from Gothic to Googie, data mining architectures have their own flavors reflecting variances in tooling, languages and infrastructure configurations across the pipeline.

The main variants include:

1. No Coupling Architecture

Like a straw hut built only from locally sourced materials, this approach relies on extracting data from files or databases into an isolated analytics engine without much integration.

Source DB/Files -> Custom Analytics Code -> CSV Outputs  

Best For: Simple analysis on small, static data samples

Pros: Quick, portable, skills-friendly

Cons: Limited scale, reliability

2. Loosely Coupled Architecture

Somewhat better than a straw hut, this model employs partial integration like APIs for coordinated data sharing across decentralized systems.

MySQL -> Custom ETL Services -> Blob Storage -> Power BI Dashboards

Best For: Medium complexity pipelines, microservices

Pros: Portable, flexible
Cons: Complex coordination, partial governance

3. Semi-tightly Coupled Architecture

Like brick and mortar houses aligned to regional locations and climate, this approach tightly integrates database capabilities for storage, indexing and retrieval into the custom data processing logic for balanced robustness and control.

Kafka -> Spark Streaming -> Hive Tables -> Custom ML on HDFS data

Best For: Most analytic pipelines

Pros: Balance of scale and control
Cons: Code upkeep across systems

4. Tightly Coupled Architectures

Like an integrated skyscraper where the foundational piles merge with the walls, frames and utilities into one unified structure, this approach intertwines the database and analytics engines for maximum interoperability.

Vertica DB Kernel -> Distributed SQL Query Processing

Best For: Massively parallel, performance-sensitive workloads

Pros: Speed, scalability
Cons: Rigidity, complexity

Choosing amongst these architectures comes down to skillsets, tooling comfort and scale needs in later sections.

But first, let‘s expand on the stages comprising end-to-end data pipelines…

Stage 1: Data Sources

The starting line of data‘s value generation journey Sources deserve first focus when creating data architecture blueprints. Aligning technical specifications to actual source systems enables smoothly ingesting raw signals.

Structuring complex source ecosystems with APIs helps, like how Amazon built centralized data APIs to tame input complexity highlighted by early AWS chief Werner Vogels:

AWS needed common data structures across all services…To handle the input firehose of data effectively, we built a set of APIs providing structured access and delivery.

Common source types include:

1. Databases – Relational (MySQL), Wide Column (Cassandra) Graph (Neo4j) etc:

[Source DB landscape]

2. Files & Objects -CSV, logs, blobs on cloud storage:

3. Streams – Clickstreams, IoT telemetry:

4. Media – Audio, images, video, HTML content:

Categorizing sources guides effective connectivity per transport mechanisms like queries, batches or streams into the staging layer.

Stage 2: Staging and Processing

With sources streaming raw signals, the heavy lifting of data transformation occurs here through ETLExtracting data from sources, Transforming into analysis-friendly format and Loading the refined oil into target datastores.

ETL Process

ETL Pipeline (Image Source: Ace Data Engineer)

Common ETL data refinement tasks include:

  • Structuring through parsing
  • Standardization to schemas
  • Error correction
  • De-duplication
  • Filtering and sampling

Platforms like Informatica, AWS Glue and Azure Data Factory provide orchestration capacities to develop managed ETL ‘jobs‘ enabling scheduling, monitoring, and maintenance simplification.

With robust ETL foundations, data quality and reliability entering storage enhances dramatically – leading us to the next stage!

Stage 3: Storage and Management

Like the Federal Reserve securely houses gold reserves underlying US currency to inspire stability and trust, carefully governed data storage buoys enterprise analytics reliant on info accuracy and accessibility.

We detail popular data store types next with governance best practices:

Data Warehouses store highly structured, analysis-ready data using schema-on-write models for efficient analytical SQL querying. Governance considerations include:

  • Access restriction to authorized users only
  • Masking sensitive fields like healthcare data
  • Maintaining query audit trails
  • Freshness via scheduled ETL refreshes

Data Lakes take the schema-on-read approach for storing mutable and multi-structured data from files to streams. Governance here handles:

  • Tagging metadata like data owner, tier sensitivity etc.
  • Logical separation by sensitivity zones
  • Table-level access controls and encryption
  • Automated pipeline testing and monitoring

Data Marts serve focused analytic workloads for specific business units with subsets of enterprise data. Governance patterns from warehouses and lakes apply based on use case dynamism.

Careful storage governance unlocks enterprise-wide data transparency while maintaining compliance and security standards – leading to smoother sailing analytical cruising with our last stage up next!

Stage 4: Consumption and Analysis

With dependable data fueling the engines, analytics dashboards, reports and models can now chart directional intelligence confidently!

Common consumption modes work as follows:

SQL Analytics offers interactive investigation for business analysts via tools like:

  • Tableau for visualizations
  • Looker for predefined metrics
  • Presto for ad-hoc querying at scale

Statistical Analytics provides both visualization and predictive models for data scientists through:

  • Jupyter Notebooks prototyping workflows in Python and R
  • Spark ML for big data machine learning

Embedded Analytics ships insights directly into consumer apps through:

  • PowerBI APIs showing reports within portals and dashboards
  • AWS QuickSight for secure cloud analytics embedding

With credible data as the foundation across storage and processing, analytics unlocks immense intelligence potential!

Now that you know the pipeline let‘s shift gears to choosing the right architecture for your needs…

How Do I Select The Right Data Mining Architecture?

With so many source systems, governance policies, ETL engines, query languages and analysis tools – how do you navigate the dizzying array of data mining architecture options?

The key is first prioritizing across 3 aspects:

1. Data Dynamics

This covers volume, variety, velocity and veracity across sources, processing and storage.

  • Architectures for small, static data can lean no or loose coupling given limited scale
  • For large, high variety and velocity pipelines tight coupling helps

2. Technology Expertise

What languages, infra and tools can your team leverage?

  • With strong SQL and database skills adopt tighter coupling
  • For distributed computing and containers skills utilize loose coupling

3. Agility Requirements

How quickly can architecture adapt to changing business scenarios?

  • Loosely coupled "Lego" like systems enable quicker change
  • Tight monoliths favor robustness over release velocity

Analyzing across these vectors helps land on an ideal starting point as this sample decision tree shows:

Architecture Decision Tree

Data Architecture Decision Flowchart

Beyond initial fit, architecting for evolution across coupling spectrums unlocks longevity.

What if you need to scale no coupling to semi-tight by adding staging and governance?

Thinking extensibly empowers adaptable blueprints!

Now that you know how to select architectures – let‘s consolidate what we know into an handy guide…

Data Mining Architecture Cheat Sheet

Here‘s a pocket-sized visual summary of everything we just covered!

Data Mining Architecture Summary

Save this for reference as you orient future data teams and application builds. With core concepts cemented, let‘s wrap up with key takeaways.

Key Takeaways

  1. Data architecture provides the technical blueprint for effective large scale data handling from raw inputs to driving intelligence through pipelines
  2. Key pipeline stages span inbound data sources, intermediate ETL, storage, security and final consumption
  3. Architecture styles range from no coupling to tight coupling reflecting integration depth with computational engines
  4. Choosing the right data architecture requires analyzing data volume and velocity needs, technology expertise and rate of business change

With these building blocks, data teams can construct sturdy pipelines improving decision making, customer service and strategic bets powered by machine learning!

Next Steps

Still have some questions on your data architecture journey? Here are expert resources worth checking out:



Conferences like Data Council and AWS re:Invent also offer cutting edge architecture talks yearly!

Thanks for joining me on this tour of all things data architecture – feel free to bookmark this guide for future reference. Here‘s wishing you the best as you craft robust large scale data ecosystems!

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