This is an excerpt from chapter 2 of my book, Data Engineering on Azure, which deals with storage. In this article we'll look at a few aspects of data ingestion: frequency and load type, and how we can handle corrupted data. We'll use Azure Data Explorer as the storage solution, but keep in mind that the same concepts apply regardless of the data fabric used. Code samples are omitted from this article, though available in the book. Let's start by looking at the frequency with which we ingest data.
Frequency defines how often we ingest a given dataset. This can range from continuous ingestion, for streaming data, to yearly ingestion - a dataset which we only need to ingest once a year. For example, our website team produces web telemetry which we can, if we want to, ingest in real time. If our analytics scenarios include some real time or near real time processing, we can bring the data into our data platform as it is being generated.
The following figure shows this streaming ingestion setup.
As users visit website pages, each visit is sent as an event to an Azure Event Hub. Azure Data Explorer ingests data into the PageViews table in real time.
Azure Event Hub is a service that can receive and process millions of events per second. An event contains some data payload sent by the client to the Event Hub. A high-traffic website could treat each page view as an event and pass the user ID, URL, and timestamp to an Even Hub. From there, data can be routed to various other services. In our case, it can be ingested in Azure Data Explorer in real time.
Another option, if we don't have any real time requirements, is to ingest the data on some regular cadence, for example every midnight we load the logs for the day.
The following figure shows this alternative setup.
Logs get copied from the website Azure Data Explorer cluster to our Azure Data Explorer cluster using an Azure Data Factory. Copy happens on a daily basis.
In this case, the website team stores its logs into a dedicated Azure Data Explorer cluster. Their cluster only stores data for the past 30 days since it is used just to measure the website performance and debug issues. Since we want to keep data for longer for analytics, we want to copy it to our cluster and preserve it there.
Azure Data Factory is the Azure ETL service, which enables serverless
data integration and transformation. We can use a Data Factory to
coordinate when and where data gets moved. In our case, we copy the logs
of the previous day every night and append them to our
Let's take another example: the sales data from our Payments team. We use this data to measure revenue and other business metrics. Since not all transactions are settled, it doesn't make sense to ingest this data daily. Our Payments team curates this data and officially publishes the financials for the previous month on the first day of each month. This is an example of a monthly dataset, one we would ingest once it becomes available, on the 1st of each month.
The following figure shows this ingestion.
Sales data gets copied from the Payments team's Azure SQL to our Azure Data Explorer cluster on a monthly cadence.
This is very similar to our previous Azure Data Factory ingestion of page view logs, the difference being the data source - in this case we ingest data from Azure SQL, and the ingestion cadence - monthly instead of daily.
Let's define the cadence of when a dataset is ready for ingestion as its grain.
The grain of a dataset specifies the frequency at which new data is ready for consumption. This can be continuous for streaming data, hourly, daily, weekly, and so on.
We would ingest a dataset with a weekly grain on a weekly cadence. The grain is usually defined by the upstream team producing the dataset. Partial data might be available earlier, but the upstream team can usually tell us when the dataset is complete and ready to be ingested.
While some data, like the logs in our example, can be ready in real time or at a daily grain, there are datasets who get updated once a year. For example, businesses use fiscal years for financial reporting, budgeting and so on. These datasets only change year over year.
Another ingestion parameter is the type of data load.
Outside of streaming data, where data gets ingested as it is produced, we have two options for updating a dataset in our system. We can perform a full load or an incremental load.
A full load means we fully refresh the dataset, discarding our current version and replacing it with a new version of the data.
For example, our Customer Success team has the list of active customer issues. As these issues get resolved and new issues appear, we perform a full load whenever we ingest the active issues into our system.
The usual pattern is to ingest the updated data into a staging table, then swap it with the destination table, as show in the following figure.
Queries are running against the ActiveIssues table. We ingest the data into the ActiveIssuesStaging table. Queries are still running against the old ActiveIssues table. We swap the two tables. Queries already started before the swap will run against the old tables, queries started after the swap will run against the new table. Finally, we can drop the old table.
Most storage solutions offer some transactional guarantees on renames to
support scenarios like this. This means if someone is running a query
ActiveIssues table, there is no chance of the query
failing due to the table not being found or of the query getting rows
from both the old and the new table. Queries running in parallel with a
rename are guaranteed to either hit the old or the new table.
The other type of data load is incremental.
An incremental load means we append data to the dataset. We start with the current version and enhance it with additional data.
Let's take as an example a
PageViews table. Since the Website team
only keeps logs around for 30 days and we want to maintain a longer
record when we ingest the data into our system, we can't fully refresh
PageViews table. Instead, every night we take the page view logs
of the previous day and we append them to the table.
One challenge of incremental loads is to figure out exactly what data is missing (that we need to append), and what data we already have. We don't want to append again data we already have, as it would create duplicates.
There are a couple of ways we can go about determining the delta between
upstream and our storage. The simplest one is contractual: the upstream
team guarantees that data will be
ready at a certain time or date.
For example, the Payments team promises that the sales data for the
previous month will be ready on the 1st, by noon. In that case, on July
1st we will load all sales data with a timestamp within June and append
it to the existing sales data we have in our system. In this case, the
delta is June sales.
Another way to determine the delta is to keep track on our side of what
is the last row we ingested and only ingest from upstream data after
this row. This is also known as a watermark. Whatever is
watermark is data we already have in our system. Upstream can have
above the watermark, which we need to ingest.
Depending on the dataset, keeping track of the watermark can be very simple or very complex. In the simplest case, if the data has a column where values always increase, we can simply see what the latest value is in our dataset and ask upstream for data with values greater than our latest.
We can then ask for page views with a timestamp greater than the watermark when we append data in our system.
Other examples of ever-increasing values are auto-incrementing columns, like the ones we can define in SQL.
Things get more complicated if there is no easy ordering of the data from which we can determine our watermark. In that case, the upstream system needs to keep track of what data it already gave us, and hand us a watermark object. When we hand back the object, upstream can determine what is the delta we need. Fortunately, this scenario is less common in the big data world. We usually have simpler ways to determine delta, like timestamps and auto-incrementing IDs.
What happens though when a data issue makes its way into the system? We got the sales data from our Payments team on July 1st, but the next day we get notified that there was an issue: somehow a batch of transactions was missing. They fixed the dataset upstream, but we already loaded the erroneous data into our platform. Let's talk about restatements and reloads.
Restatements and reloads
In a big data system, it is inevitable that at some point, some data gets corrupted, or is incomplete. The owners of the data fix the problem, then issue a restatement.
A restatement of a dataset is a revision and re-release of a dataset after one or more issues were identified and fixed.
Once data is restated, we need to reload it into our data platform. This is obviously much simpler if we perform a full load for the dataset. In that case, we simply discard the corrupted data we previously loaded and replace it with the restated data.
Things get more complicated if we load this dataset incrementally. In that case, we need to drop only the corrupted slice of the data and reload that from upstream. Let's see how we can do this in Azure Data Explorer .
Azure Data Explorer stores data in extents. An extent is a shard of the data, a piece of a table which contains some of its rows. Extents are immutable - once written, they are never modified. Whenever we ingest data, one or more extents are created. Periodically, Azure Data Explorer merges extents to improve query performance. This is handled by the engine in the background.
The following figure shows how extents are created during ingestion, then merged by Azure Data Explorer.
Extents are created during ingestion, then merged by Azure Data Explorer to improve query performance
While we can't modify an extent, we can drop it. Dropping an extent
removes all data stored within. Extents support tagging, which enable us
to attach metadata to them. A best practice is to add the
to extents on creation. This tag has special meaning for Azure Data
Explorer: it will only merge extents with the same
drop-by tag. This
will ensure that all data ingested into an extent with a
is never grouped with data ingested with another
The following figure shows how we can use this tag to ensure data doesn't get mixed, then we can drop extents with that tag to remove corrupted data.
We ingested 2 extents with drop-by tag 2020-06-29 and 2 extents with drop-by tag 2020-06-30. They get merged into 1 extent with drop-by tag 2020-06-29 and 1 extent with drop-by tag 2020-06-30. We can ask Azure Data Explorer to drop all extents tagged with 2020-06-29 to remove a part of the data.
drop-by tag ensures that extents with different values for the tag
never get merged together, so we don't risk dropping more data than
what we want dropped. The value of the tag is arbitrary, we can use
anything, but a good practice is to use an ingestion timestamp. So for
example when we load data on 2020-06-29, we use the
If we later learn that the data we loaded was corrupted and upstream restates the data, we can drop the extents containing corrupted data and re-ingest from upstream to repair our dataset.
Obviously, this process is more complicated than if we were doing a full load of the data every time. In general, if we can afford a full load, we should use that. Maintenance-wise, it is a much simpler approach. Sometimes though, this is impossible - for example if we want to maintain page view logs beyond the 30-day retention period upstream has, we can't keep reloading the data. Other times, full load is just too expensive: we end up moving the same gigabytes of data again and again, with minor differences. For these situations, we have to look at an incremental load and manage the additional complexity.
- We can ingest data continuously (streaming), or at a certain regular cadence like daily, weekly, monthly, or yearly.
- When we ingest data, we can perform either a full load, or we can perform an incremental load.
- A full load means we fully refresh the dataset, discarding our current version and replacing it with a new version of the data.
- An incremental load means we append data to the dataset. We start with the current version and enhance it with additional data.
- It is inevitable for some data to get corrupted. Once repaired upstream, we need a way to discard the corrupted data from our system and reload the updated data.