I have been following Microsoft's tutorial to incrementally/delta load data from an SQL Server database.
It uses a watermark (timestamp) to keep track of changed rows since last time. The tutorial stores the watermark to an Azure SQL database using the "Stored Procedure" activity in the pipeline so it can be reused in the next execution.
It seems overkill to have an Azure SQL database just to store that tiny bit of meta information (my source database is read-only btw). I'd rather just store that somewhere else in Azure. Maybe in the blob storage or whatever.
In short: Is there an easy way of keeping track of this type of data or are we limited to using stored procs (or Azure Functions et al) for this?
I had come across a very similar scenario, and from what I found you can't store any watermark information in ADF - at least not in a way that you can easily access.
In the end I just created a basic tier Azure SQL database to store my watermark / config information on a SQL server that I was already using in my pipelines.
The nice thing about this is when my solution scaled out to multiple business units, all with different databases, I could still maintain watermark information for each of them by simply adding a column that tracks which BU that specific watermark info was for.
Blob storage is indeed a cheaper option but I've found it to require a little more effort than just using an additional database / table in an existing database.
I agree it would be really useful to be able to maintain a small dataset in ADF itself for small config items - probably a good suggestion to make to Microsoft!
There is a way to achieve this by using Copy activity, but it is complicated to get latest watermark in 'LookupOldWaterMarkActivity', just for reference.
Dataset setting:
Copy activity setting:
Source and sink dataset is the same one. Change the expression in additional columns to #{activity('LookupNewWaterMarkActivity').output.firstRow.NewWatermarkvalue}
Through this, you can save watermark as column in .txt file. But it is difficult to get the latest watermark with Lookup activity. Because your output of 'LookupOldWaterMarkActivity' will be like this:
{
"count": 1,
"value": [
{
"Prop_0": "11/24/2020 02:39:14",
"Prop_1": "11/24/2020 08:31:42"
}
]
}
The name of key is generated by ADF. If you want to get "11/24/2020 08:31:42", you need to get column count and then use expression like this: #activity('LookupOldWaterMarkActivity').output.value[0][Prop_(column count - 1)]
How to get latest watermark:
use GetMetadata activity to get columnCount
use this expression:#activity('LookupOldWaterMarkActivity').output.value[0][concat('Prop_',string(sub(activity('Get Metadata1').output.columnCount,1)))]
Related
I have a blob storage container in Azure Data Factory that contains many JSON files. Each of these JSON files contain data from an API. The data needs to be split into ~30 tables in my azure DWH.
ADF Processhere
I am hoping someone can provide some clarity on the best way to achieve this (I am new to the field of Data Engineering and trying to develop my skills through projects).
At present, I have written 1 stored procedure which contains code to extract data and insert data into 1 of the 30 tables. Is this the right approach? And if so, could you please advise how best to design my pipeline?
Thanks in advance.
I am assuming that the you also have 30 folders in the containers and eachdata from each folder will land up in a table or may be the file are named in a such a way that you can dereive which table it lands on . Please read about Get meta data activity and it should give an idea as to how to get the filename/folder name : https://learn.microsoft.com/en-us/azure/data-factory/control-flow-get-metadata-activity
Once you have that I think you should read about paramterizing a dataset : Use parameterized dataset for DataFlow and this also https://www.youtube.com/watch?v=9XSJih4k-l8
I'm trying to move some data from Azure SQL Server Database to Azure Blob Storage with the "Copy Data" pipeline in Azure Data Factory. In particular, I'm using the "Use query" option with the ?AdfDynamicRangePartitionCondition hook, as suggested by Microsoft's pattern here, in the Source tab of the pipeline, and the copy operation is parallelized by the presence of a partition key used in the query itself.
The source on SQL Server Database consists of two views with ~300k and ~3M rows, respectively.
Additionally, the views have the same query structure, e.g. (pseudo-code)
with
v as (
select hashbyte(field1) [Key1], hashbyte(field2) [Key2]
from Table
)
select *
from v
and so do the tables that are queried by the views. On top of this, the views query the same number of partitions with a roughly equally distributed number of rows.
The unexpected behavior - most likely due to the lack of experience from my side - of the copy operation is that it lasts much longer for the view that query fewer rows. In fact, the copy operation with ~300k rows shows a throughput of ~800 KB/s, whereas the one with ~3M rows shows a throughput of ~15MB/s (!). Lastly, the writing operation to the blob storage is pretty fast for both cases, as opposite to the reading-from-source operation.
I don't expect anyone to provide an actual solution - as the information provided is limited -, but I'd rather like some hints on what could be affecting the copy performance so badly for the case where the view queries much (roughly an order of magnitude) fewer rows, taking into account that the tables under the views have a comparable number of fields, and also the same data types: both the tables that the views query contain int, datetime, and varchar data types.
Thanks in advance for any heads up.
To whoever might stumble upon the same issue, I managed to find out, rather empirically, that the bottleneck was being caused by the presence of several key-hash computations in the view on SQL DB. In fact, once I removed these - calculated later on Azure Synapse Analytics (data warehouse) - I observed a massive performance boost of the copy operation.
When there's a copy activity performance issue in ADF and the root cause is not obvious (e.g. if source is fast, but sink is throttled, and we know why) -- here's how I would go about it :
Start with the Integration Runtime (IR) (doc.). This might be a jobs' concurrency issue, a network throughput issue, or just an undersized VM (in case of self-hosted). Like, >80% of all issues in my prod ETL are caused by IR-s, in one way or another.
Replicate copy activity behavior both on source & sink. Query the views from your local machine (ideally, from a VM in the same environment as your IR), write the flat files to blob, etc. I'm assuming you've done that already, but having another observation rarely hurts.
Test various configurations of copy activity. Changing isolationLevel, partitionOption, parallelCopies and enableStaging would be my first steps here. This won't fix the root cause of your issue, obviously, but can point a direction for you to dig in further.
Try searching the documentation (this doc., provided by #Leon is a good start). This should have been a step #1, however, I find ADF documentation somewhat lacking.
N.B. this is based on my personal experience with Data Factory.
Providing a specific solution in this case is, indeed, quite hard.
I'm designing Data Factory piplelines to load data from Azure SQL DB to Azure Data Factory.
My initial load/POC was a small subset of data and was able to load from SQL tables to Azure DL.
Now, there are huge volume of tables (that has even billion +) that I want to load from SQL DB using DF to Azure DL.
MS docs mentioned two options, i.e. watermark columns and change tracking.
Let's say I have a "cust_transaction" table that has millions of rows and if I load to DL then it loads as "cust_transaction.txt".
Questions.
1) What would an optimal design to incrementally load the source data from SQL DB into that file in the data lake?
2) How do I split or partition the files into smaller files?
3) How should I merge and load the deltas from source data into the files?
Thanks.
You will want multiple files. Typically, my data lakes have multiple zones. The first zone is Raw. It contains a copy of the source data organized into entity/year/month/day folders where entity is a table in your SQL DB. Typically, those files are incremental loads. Each incremental load for an entity has a file name similar to Entity_YYYYMMDDHHMMSS.txt (and maybe even more info than that) rather than just Entity.txt. And the timestamp in the file name is the end of the incremental slice (max possible insert or update time in the data) rather than just current time wherever possible (sometimes they are relatively the same and it doesn't matter, but I tend to get a consistent incremental slice end time for all tables in my batch). You can achieve the date folders and timestamp in the file name by parameterizing the folder and file in the dataset.
Melissa Coates has two good articles on Azure Data Lake: Zones in a Data Lake and Data Lake Use Cases and Planning. Her naming conventions are a bit different than mine, but both of us would tell you to just be consistent. I would land the incremental load file in Raw first. It should reflect the incremental data as it was loaded from the source. If you need to have a merged version, that can be done with Data Factory or U-SQL (or your tool of choice) and landed in the Standardized Raw zone. There are some performance issues with small files in a data lake, so consolidation could be good, but it all depends on what you plan to do with the data after you land it there. Most users would not access data in the RAW zone, instead using data from Standardized Raw or Curated Zones. Also, I want Raw to be an immutable archive from which I could regenerate data in other zones, so I tend to leave it in the files as it landed. But if you found you needed to consolidate there, that would be fine.
Change tracking is a reliable way to get changes, but I don't like their naming conventions/file organization in their example. I would make sure your file name has the entity name and a timestamp on it. They have Incremental - [PipelineRunID]. I would prefer [Entity]_[YYYYMMDDHHMMSS]_[TriggerID].txt (or leave the run ID off) because it is more informative to others. I also tend to use the Trigger ID rather than the pipeline RunID. The Trigger ID is across all the packages executed in that trigger instance (batch) whereas the pipeline RunID is specific to that pipeline.
If you can't do the change tracking, the watermark is fine. I usually can't add change tracking to my sources and have to go with watermark. The issue is that you are trusting that the application's modified date is accurate. Are there ever times when a row is updated and the modified date is not changed? When a row is inserted, is the modified date also updated or would you have to check two columns to get all new and changed rows? These are the things we have to consider when we can't use change tracking.
To summarize:
Load incrementally and name your incremental files intelligently
If you need a current version of the table in the data lake, that is a separate file in your Standardized Raw or Curated Zone.
I am building data lake to integrate multiple data sources for advanced analytics.
In the begining, I select HDFS as data lake storage. But I have a requirement for updates and deletes in data sources which I have to synchronise with data lake.
To understand the immutable nature of Data Lake I will consider LastModifiedDate from Data source to detect that this record is updated and insert this record in Data Lake with a current date. The idea is to select the record with max(date).
However, I am not able to understand how
I will detect deleted records from sources and what I will do with Data Lake?
Should I use other data storage like Cassandra and execute a delete command? I am afraid it will lose the immutable property.
can you please suggest me good practice for this situation?
1. Question - Detecting deleted records from datasources
Detecting deleted records from data sources, requires that your data sources supports this. Best is that deletion is only done logically, e. g. with a change flag. For some databases it is possible to track also deleted rows (see for example for SQL-Server). Also some ETL solutions like Informatica offer CDC (Changed Data Capture) capabilities.
2. Question - Changed data handling in a big data solution
There are different approaches. Of cause you can use a key value store adding some kind of complexity to the overall solution. First you have to clarify, if it is also of interest to track changes and deletes. You could consider loading all data (new/changed/deleted) into daily partitions and finally build an actual image (data as it is in your data source). Also consider solutions like Databricks Delta addressing this topics, without the need of an additional store. For example you are able to do an upsert on parquet files with delta as follows:
MERGE INTO events
USING updates
ON events.eventId = updates.eventId
WHEN MATCHED THEN
UPDATE SET
events.data = updates.data
WHEN NOT MATCHED
THEN INSERT (date, eventId, data) VALUES (date, eventId, data)
If your solution also requires low latency access via a key (e. g. to support an API) then a key-values store like HBase, Cassandra, etc. would be helpfull.
Usually this is always a constraint while creating datalake in Hadoop, one can't just update or delete records in it. There is one approach that you can try is
When you are adding lastModifiedDate, you can also add one more column naming status. If a record is deleted, mark the status as Deleted. So the next time, when you want to query the latest active records, you will be able to filter it out.
You can also use cassandra or Hbase (any nosql database), if you are performing ACID operations on a daily basis. If not, first approach would be your ideal choice for creating datalake in Hadoop
Not really sure this is an explicit question or just a query for input. I'm looking at Azure Data Factory to implement a data migration operation. What I'm trying to do is the following:
I have a No SQL DB with two collections. These collections are associated via a common property.
I have a MS SQL Server DB which has data that is related to the data within the No SQL DB Collections via an attribute/column.
One of the NoSQL DB collections will be updated on a regular basis, the other one on a not so often basis.
What I want to do is be able to prepare a Data Factory pipline that will grab the data from all 3 DB locations combine them based on the common attributes, which will result in a new dataset. Then from this dataset push the data wihin the dataset to another SQL Server DB.
I'm a bit unclear on how this is to be done within the data factory. There is a copy activity, but only works on a single dataset input so I can't use that directly. I see that there is a concept of data transformation activities that look like they are specific to massaging input datasets to produce new datasets, but I'm not clear on what ones would be relevant to the activity I am wanting to do.
I did find that there is a special activity called a Custom Activity that is in effect a user defined definition that can be developed to do whatever you want. This looks the closest to being able to do what I need, but I'm not sure if this is the most optimal solution.
On top of that I am also unclear about how the merging of the 3 data sources would work if the need to join data from the 3 different sources is required but do not know how you would do this if the datasets are just snapshots of the originating source data, leading me to think that the possibility of missing data occurring. I'm not sure if a concept of publishing some of the data someplace someplace would be required, but seems like it would in effect be maintaining two stores for the same data.
Any input on this would be helpful.
There are a lot of things you are trying to do.
I don't know if you have experience with SSIS but what you are trying to do is fairly common for either of these integration tools.
Your ADF diagram should look something like:
1. You define your 3 Data Sources as ADF Datasets on top of a
corresponding Linked service
2. Then you build a pipeline that brings information from SQL Server into a
temporary Data Source (Azure Table for example)
3. Next you need to build 2 pipelines that will each take one of your NoSQL
Dataset and run a function to update the temporary Data Source which is the ouput
4. Finally you can build a pipeline that will bring all your data from the
temporary Data Source into your other SQL Server
Steps 2 and 3 could be switched depending on which source is the master.
ADF can run multiple tasks one after another or concurrently. Simply break down the task in logical jobs and you should have no problem coming up with a solution.