EXECUTIVE OVERVIEW
Analytical Ants provides systems (“Ants”) that systematically increase operational efficiencies and yields through dynamic data insights, data architecture, and processes.
We deliver these insights through a holistic approach encompassing a large portion of the data-pipeline, mainly through warehousing, machine learning, and reporting.
Example services for Warehousing and Machine Learning: supervised & unsupervised ML data analysis, ML server integration, star schema data modeling, implementing enterprise data warehouse bus architecture, server object scripting and scheduling (ETL).
Example services for Analysis Services & Reporting (PBI/Excel): (1) Architectural Planning: Server Selection (on-prem vs. premium), Load Balancing, Deployments Strategies, Monitoring Tabular Solutions, Security, (2) Data Modeling: Model Selection & Procurement, Partitioning Strategies, Model Optimizations, Ingestion Strategies, Relationship Design, and (3) Reporting: Visualizations, DAX optimizations, JSON configurations, Report Optimizations & Design.
Connect with us today to partner in ‘Procuring Insights for YOUR Success’!
THE DETAILS
The Details:
Ask 100 people about data architecture, and surely you will get as many responses.
I appreciate Machine Learning Guides’1 response to this question and loosely based my overview on their response.
There are four stages in the data pipeline (i.e., from data ingestion to data consumption): Data Sources, Data Lake, Data Warehouse, and Data Analysis (image below).
- Data Sources: In this context, this is the origin of data2. Examples could include other structured/unstructured data sources, data feeds, etc.
- Data Lake: This is the aggregation of data sources in an ‘unstructured/unclean’ repository. ‘Unified tooling’ may be applied such as permissions or common schematics; feature engineering can be applied here too.
- Data Warehouse: This is a more ‘structured/clean’ repository where data is held in a columnar format. Data held here will be utilized by analysts or machine learning models.
- Data Analysis: This is where transformed data makes an impact. Whether through applications, visualizations, or notifications – integrated data analysis helps drive businesses forward.
Analytical Ants specializes in the Data Warehouse and Data Analysis sections of the Data Pipeline (see image above). This cross section is where we procure insights for your success, ultimately adding value to your organization.
- Data Warehouse
- Databasing: We design and schedule ETL packages for data materialization to meet SLAs.
- Machine Learning: We engineer machine learning models and integrate them into your systems to ensure up-to-date data insights.
- Data Analysis
- Analysis: We architect tabular solutions to ensure responsive, performant, reliant, and secure data consumption.
- Visualizations: We build industry-leading reports for ease of use and exploration.
Our goal is to automate your data architecture to provide consistent and reliable insights that will drive your business further.
Interested? Partner with us to make the most of your data!
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References
- Machine Learning Guide, Spotify, “MLG 002 What is AI, ML, DS”
- https://open.spotify.com/episode/1o7YUm1lD9AHyU1j7OCqgc?si=c3440f7ba646409d
- Talend, “What is a data source”
- https://www.talend.com/resources/data-source/#:~:text=A%20data%20source%20is%20the,process%20accesses%20and%20utilizes%20it
- Wikipedia, “Data Lake”
- https://en.wikipedia.org/wiki/Data_lake
