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compiled a quick list of terms to help understand this piece: MDS - modern data stack BI - business intelligence Redshift - amazon redshift data warehouse se

compiled a quick list of terms to help understand this piece: MDS - modern data stack BI - business intelligence Redshift - amazon redshift data warehouse service product handles large scale data sets and database migrations can handle analytic workoads on big data sets with column oriented approach built on top of massive parallel processing (MPP) from data warehouse company ParAccel (later acquired by Actian) ETL - extract, transform, load ELT - extract, load, transform -- an alternative to ETL that stores raw data Looker - does BI, started with looker data sciences, acquired by google Tableau - data visualization focused on business intelligence clickstream data - user website navigation records snowflake - an MDS data platform solution mongo - nosql database datadog - monitoring, analytics for devops confluent - real time data streams databricks - web based cluster management and data lakes for machine learning meme-ification - reduction of an idea to cartoons peak - highest point preceeding drop off fivetran - data ingestion dbt - dbt labs, data transformation CDO - chief data officer co-marketing - integrated marketing such as linked brands co-sponsored - cooperative sponsorship co-selling - shared sales assets including data records ARR - annual recurring revenue ZIRP - zero interest rate policy private multiples - private company valuation calculations, metrics forward revenue - revenue expected in the future PowerBI - microsoft business analytics

bigger_cheese 3y ago
I think you need to go a step further and define a few more terms like what you mean when you say "Analytics" and "business intelligence" For example >PowerBI - microsoft business analytics I don't think I would classify Power BI as an analytics tool - In my limited experience with using it I have found that it has some nice ways to build reports and visualize data graphically but only very rudimentary statistical functions and is not really suited for performing data analysis. My organization is travelling along the "modern data" pipeline so I'm grappling with some of the same confusion myself about what everything is in the new world... In the "old data" world I'm familiar with - I would use stuff like SAS or R to perform ad-hoc analytics i.e. analyze data using statistics, perform regressions, Timeseries forecasting, distribution analysis etc. For reporting and visualizing data: that would fall to things like excel, Cognos, ggplot2, Proc report and Some javascript plotting packages like d3js etc. To Extract data would involve running SQL query against databases (Oracle, DB2 or Microsoft SQL Server etc directly) directly then using sas or R to transform the data as needed. Modern approach seems to be Databricks to ingest data into datalake, Python via Juypter notebooks to perform analytics hosted on Azure ML studio running in cloud on a "compute instance" for another level of Indirection...Then Power BI to visualize and build reports.

bigger_cheese 3y ago

I think you need to go a step further and define a few more terms like what you mean when you say "Analytics" and "business intelligence" For example >PowerBI - microsoft business analytics I don't think I would classify Power BI as an analytics tool - In my limited experience with using it I have found that it has some nice ways to build reports and visualize data graphically but only very rudimentary statistical functions and is not really suited for performing data analysis. My organization is travelling along the "modern data" pipeline so I'm grappling with some of the same confusion myself about what everything is in the new world... In the "old data" world I'm familiar with - I would use stuff like SAS or R to perform ad-hoc analytics i.e. analyze data using statistics, perform regressions, Timeseries forecasting, distribution analysis etc. For reporting and visualizing data: that would fall to things like excel, Cognos, ggplot2, Proc report and Some javascript plotting packages like d3js etc. To Extract data would involve running SQL query against databases (Oracle, DB2 or Microsoft SQL Server etc directly) directly then using sas or R to transform the data as needed. Modern approach seems to be Databricks to ingest data into datalake, Python via Juypter notebooks to perform analytics hosted on Azure ML studio running in cloud on a "compute instance" for another level of Indirection...Then Power BI to visualize and build reports.