Data Ingestion and Processing

What it is

Large scale analytics begins with getting data out of its sources and into a form that people can analyse. Ingestion brings the data in. Processing cleans, joins and reshapes it.

Both steps are automated with pipelines, so they run on a schedule or as data arrives, without anyone copying files by hand.

How it works

Data sources and ingestion

Source data comes from transactional databases, files, applications, devices and external feeds. Ingestion copies it into a central analytical store, either on a schedule in batches or continuously as a stream. A common first stop is a data lake, where raw files land untouched so nothing is lost.

Pipelines

A data pipeline is a defined sequence of steps that moves and prepares data. It is made of activities, such as copying data, running a transformation, or calling a notebook. A pipeline can be started by a trigger, for example every night at 02:00, or when a new file arrives.

Azure services that build pipelines are:

• Azure Data Factory, a managed service for data integration.
• Data Factory in Microsoft Fabric, with the same pipeline ideas inside Fabric.
• Azure Synapse Analytics pipelines, built into Synapse.

A pipeline usually connects to its sources through connectors, so it can read from databases, storage accounts and many other systems.

ETL and ELT

ELT is common in cloud analytics because a lake or warehouse can transform huge amounts of data in parallel, and keeping the raw data lets you reprocess it later.

Processing the data

Typical transformations include:

• Cleaning: removing duplicates, fixing formats and handling missing values.
• Joining: combining data from several sources.
• Aggregating: totalling or averaging to a coarser level.
• Reshaping: turning raw records into tables fit for a star schema.

The work is done by an engine suited to the data size and language. Apache Spark processes large datasets in parallel with code in Python, Scala, SQL or R, and it is available in Azure Databricks, Azure Synapse Analytics and Microsoft Fabric. SQL engines transform data with T-SQL, and visual dataflows let people transform data without writing code.

Data quality and security

Cleaning matters because reports built on wrong data mislead decisions. Teams also apply security during processing, for example masking personal data and limiting who can read each store.

A worked scenario

Every night a retailer needs yesterday's sales from 200 stores in its reporting system. A pipeline starts at 02:00. A copy activity ingests the sales files into a data lake. A Spark notebook then removes duplicate rows, fixes date formats and joins the sales to product details. The result is saved as a curated table that the reporting team reads in the morning. The same pipeline runs every night with no manual steps, and failures raise an alert.

A pipeline in numbered steps

1. A trigger fires at 02:00 each night.
2. A copy activity reads yesterday's sales files from a storage account and writes them to the lake, unchanged.
3. A notebook or dataflow cleans the data and joins it to the product table.
4. A final activity loads the result into a warehouse table.
5. A failure at any step sends an alert, and the run can be repeated safely.

Full load or incremental load

An incremental load saves time and money, but a mistake in how changes are detected can leave rows missing.

Where raw data lands

Many teams organise a lake in layers. A first layer holds raw data exactly as received, so nothing is lost. A second layer holds cleaned and validated data. A third layer holds curated tables ready for reporting. If a transformation turns out to be wrong, the pipeline can rerun from the raw layer.

Monitoring and cost

Pipelines need monitoring, because a quiet failure means reports show old data. Services report run history, durations and errors, and alerts can notify the data engineer. Costs depend on the amount of data moved and the compute used, so scheduling work for quiet hours and loading only what changed keeps the bill down.