Anomaly detection is a very common use case in IoT related deployments. A new ANOMALYDETECTION operator has been recently added into Azure Stream Analytics and is currently at public preview. ANOMALYDETECTION operator detects anomalies based on Exchangeability Martingales (EM) that supports online test of the exchangeability of a sequence of event values. When the distribution of the sequence … Continue reading Anomaly Detection with Azure Stream Analytics
Tag: Data Engineering
SSIS in Azure #1 – Periodically Ingesting Data from SQL Database into Azure Data Lake using SSIS
*The source code created for this blog post is located here. The low cost, schema-less and large column attributes of Azure Data Lake Store along with the large number of supported analytic engines (e.g., Azure Data Lake Analytics, Hive and Spark) makes it a prefect store-everything repository for enterprise data. We can offline the copies of business … Continue reading SSIS in Azure #1 – Periodically Ingesting Data from SQL Database into Azure Data Lake using SSIS
End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 4)
This is the last part of the blog series demonstrating how to build an end-to-end ADF pipeline for data warehouse ELT. Introduction & Preparation Build ADF pipeline for dimension tables ELT Build ADLA U-SQL job for incremental extraction of machine cycle data Build ADF pipeline for fact table ELT In the previous part we created … Continue reading End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 4)
End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 3)
This is the third part of the blog series to demonstrate how to build an end-to-end ADF pipeline for data warehouse ELT. The part will describe how to build an ADLA U-SQL job for incremental extraction of machine cycle data from Azure Data Lake store and go through the steps for scheduling and triggering the … Continue reading End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 3)
End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 2)
This is the second part of the blog series to demonstrate how to build an end-to-end ADF pipeline for extracting data from Azure SQL DB/Azure Data Lake Store and loading to a star-schema data warehouse database with considerations on SCD (slow changing dimensions) and incremental loading. Introduction & Preparation Build ADF pipeline for dimensional tables … Continue reading End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 2)
End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 1)
This blog series demonstrates how to build an end-to-end ADF pipeline for extracting data from Azure SQL DB/Azure Data Lake Store and load to a star-schema data warehouse database with considerations of SCD (slow changing dimensions) and incremental loading. The final pipeline will look as: The machine cycle records will be load from the csv … Continue reading End-to-End Azure Data Factory Pipeline for Star Schema ETL (Part 1)
Trigger Azure Analysis Service Processing in Azure Data Factory
There is one important feature missing from Azure Data Factory. In SSIS, at the end of the ETL process when the new data has been transformed and load into data warehouse, the SSAS processing task can be run to process the cube immediately after the new data has flow into the data warehouse. However, Azure … Continue reading Trigger Azure Analysis Service Processing in Azure Data Factory
Handling Across-Day Cycle Issue in Daily Usage Analysis using U-SQL
When analysing daily usage of a machine that can run across days, we need to split the time of a single machine running cycle into right days. As the example below shows, the second machine (D00002) run across two days and the third machine (D00003) run across three days. To analyse the daily usage of … Continue reading Handling Across-Day Cycle Issue in Daily Usage Analysis using U-SQL
Generate Device Cycle Records from Raw Telemetry Message using Azure Data Lake Analytics
The raw telemetry data collected from IoT sensor is normally event-based, e.g., a "Device On" message when the device starts to run, and a "Device Off" message when the device stops. One common data preprocessing task is to transform the raw "On/Off" telemetry data into device cycle records with the start time, end time and … Continue reading Generate Device Cycle Records from Raw Telemetry Message using Azure Data Lake Analytics
Data Ninjago (Financial Data Architecture) 
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