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Category: ETL / ELT

FlowFile Continuation In NiFi

Published 2017-05-02 by Kevin Feasel

Tim Spann describes one of the more powerful features of Apache NiFi:

Sometimes, you need to backup your current running flow, let that flow run at a later date, or make a backup of what is in-process now. You want this in a permanent storage and want to reconstitute it later like orange juice and add it back into the flow or restart it.

This could be due to failures, for integration testing, for testing new versions of components, as a checkpoint, or for many other purposes. You don’t always want to reprocess the original source or files (they may be gone).

Read on for an explanation of how FlowFile streams can do this.

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Moving Files In Azure Data Factory

Published 2017-04-12 by Kevin Feasel

Meagan Longoria has a workaround for how you cannot move a file using Azure Data Factory:

But at this time ADF doesn’t support that. You can copy a file with a copy activity, but you cannot actually move (i.e., copy and delete).

Luckily, we had a workaround for our situation. If you tell ADF to copy data to a file that already exists in the specified location in the data lake, it will overwrite the existing file. We made sure the file name is always the same for each table in the staging area so there is always only one file per table.

Read on for the full details on this workaround.  Also, vote on this feedback item if you want the ability to move files instead of just copying them.

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Shredding Excel With R

Published 2017-01-18 by Kevin Feasel

John MacKintosh shows how to use R for wrangling + ETL:

I had over 140 files to process. That’s not usually a big deal – I normally use SQL Server Integration Services to loop through network folders, connect to hundreds of spreadsheets and extract the source data.

But this relies on the data being in a tabular format (like a dataframe or database table).

A quick glance at the first few sheets confirmed I could not use this approach – the data was not in tabular format. Instead it was laid out in a format suited to viewing the data on screen – with the required data scattered in different ranges throughout each sheet ( over 100 rows and many columns). It wasn’t going to be feasible to point SSIS at different locations within each sheet. (It can be done, but it’s pretty complex and I didn’t have time to experiment).

The other challenge was that over time, changes to design meant that data moved location e.g. dates that were originally in cell C2 moved to D7, then moved again as requirements evolved. There were 14 different templates in all, each with subtle changes. Each template was going to need a custom solution to extract the data.

This is a good look at how R can be about more than “just” statistical analysis.

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Importing CSV Files In Power BI

Published 2016-12-08 by Kevin Feasel

Gil Raviv explains the new “combine binaries” feature of Power BI Desktop:

The Power BI team has recently released an enhanced “combine binaries” experience as part of November 2016 update to Power BI Desktop. (Jargon Alert:  “Combine Binaries” is a scary term.  Instead it should be named “Magically combine multiple files together into one table and make me SUPER happy.”)  The improved experience can drastically help you to import multiple Excel or other files from a folder and avoid writing advanced query functions. But today we will focus on a specific scenario, which is so common that it deserves this special post – Handling CSV files.

In fact, today’s blog post is actually the first post in “The CSV Series”. I hope you will enjoy it. To celebrate the November update of Power BI Desktop, we will review the improved experience, and will walk you through one of the most common scenarios that is now so easy to implement – Importing multiple CSV files from a folder, including parts of their filenames.

This looks very useful.

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Quickly Reloading Tables

Published 2016-11-22 by Kevin Feasel

Kenneth Fisher uses table partitioning to perform fast loads of data:

Now if this table is paritioned you’d use SWITCH and bring in a new partition.

For those that don’t know, when a table is partitioned, you can create a new empty partition, and a new empty table, load the table, make the table exactly match the partition (structure, check constraints, & indexes for example) and you can SWITCH it in. The SWITCH part is a metadata operation and is fast!

But what do you do if the table isn’t partitioned? Well, I was having a conversation with Andy Mallon (b/t) and he reminded me of something.

Read on for the details.  The upshot is that you can take your time loading the second table and once you’re ready to swap out, it’s a quick metadata change.  That’s really useful for ETL scenarios.

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Exporting To Flat Files

Published 2016-11-03 by Kevin Feasel

Ben Weissman shows how to dump tables to flat files:

In our next step, we loop through all tables in that database (feel free to limit the results by playing with GetDatabaseSchema) and create a FlatFileFormat for each of them. We will include all columns except those with datatype Binary or Object. As flatfiles don’t really care about actual data formats, we will just define every column as a string with maximum length. We will also add an annotation with the table’s original name, the list of columns as well as a list of primary keys (we’ll need the latter for a later step :)):

Like most Biml-related things, it’s not that many lines of code, so check it out.

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Bulk Load Tools

Published 2016-10-21 by Kevin Feasel

Erland Sommarskog has a brand new essay:

The bulk-load tools have been in the product for a long time and they are showing their age. When they work for you, they are powerful. But you need to understand that these tools are binary to their heart, and they have no built-in rule that says that each line a file is a record – they don’t even think in lines. You also need to understand that there are file formats they are not able to handle.

I have tried to arrange the material in this article so that if you have a simple problem, you only need to read the first two chapters after the introduction. I first introduce you them to their mindset, which is likely to be different from yours. Next I cover the basic options to use for every-day work. If you have a more complex file, you will need to use a format file and the next three chapters are for you. I first describe how format files work as such, and the next two chapters show how to use format files for common cases for import and export respectively. This is followed by a chapter about Unicode files, including files encoded in UTF‑8. Then comes a chapter about “advanced” options, including how to load explicit values into an IDENTITY column. A short chapter covers permissions. The last chapter discusses XML format files, and I am not sorry at all if you give this chapter a blind eye – I find XML format files to be of dubious value.

I haven’t had a chance to read this yet, but because I have never had good luck with bcp and BULK INSERT, it’s on my to-read list.

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Traversing Foreign Keys Using Biml

Published 2016-09-28 by Kevin Feasel

Ben Weissman has a two-part series on loading a set of tables based on foreign key constraints.  Part 1 is linear loads:

All our previous posts were running data loads in parallel, ignoring potential foreign key constraints. But in real life scenarios, your datawarehouse may actually have tables refering to each other using such, meaning that it is crucial to create and populate them in the right order.

In this blog post, we’ll actually solve 2 issues at once: We’ll provide a list of tables, will then identify any tables that our listed tables rely on (recursively) and will then create and load them in the right order.

In this sample, we’ll use AdventureWorksDW2014 as our source and transfer the FactInternetSales-table as well as all tables it is connected to through foreign key constraints. Eventually, we will create all these tables including the constraints in a new database AdventureWorksDW2014_SalesOnly (sorting them so we get no foreign key violations) and eventually populate them with data.

Part 2 is parallel loads:

After the first excitment about how easy it actually was to take care of that topology, you might ask yourself: Why does it have to run linear? That takes way too long. And you’re right – and it doesn’t have to.

All we need to do is:

– Create a list of all the tables that we’ve already loaded (which will be empty at that point)
– Identify all tables that do not reference any other tables
– Load these tables, each followed by all tables that only reference this single table – recursively and add them to list of loaded tables
– Once that is done, load all tables that are referencing multiple tables where all required tables have been loaded before – and again, add them to the list
– Repeat this until no table is left to load (or for a maximum of 10 times in this example)
– If, for whichever reason, any tables are left, load them sequentially using the TopoSort function:

This is a very interesting way of using Biml to traverse the foreign key tree.  I’ve normally used recursive CTEs in T-SQL to do the same, but I’ll have to play around with this method.

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Change Data Capture With Apache NiFi

Published 2016-09-15 by Kevin Feasel

Satish Bomma uses Apache NiFi to perform change data capture on a MySQL database:

The main things to configure is DBCPConnection Pool and Maximum-value Columns

Please choose this to be the date-time stamp column that could be a cumulative change-management column

This is the only limitation with this processor as it is not a true CDC and relies on one column. If the data is reloaded into the column with older data the data will not be replicated into HDFS or any other destination.

This processor does not rely on Transactional logs or redo logs like Attunity or Oracle Goldengate. For a complete solution for CDC please use Attunity or Oracle Goldengate solutions.

That last paragraph in the snippet is key:  it’s not a true replacement for CDC-friendly products.  It is, however, a good example for showing how to use NiFi to connect to a relational database and pump data out of it.

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