Like containers and codecs are sometimes not compatible with each other, some codecs do not work with arbitrary dimensions. So, try to stick with common dimensions or research the limitations of the codec you are trying to use. Presets to change between a fast encode bigger file size and more compression smaller file size. The number of pictures per Group of Pictures. A higher number generally leads to a smaller file but needs a higher-powered device to replay it.
Sets the average bit rate quality , which is the count of binary digits per frame. See also: FFmpeg -b:v. Video files can use what is called variable bit rate VBR. This is used to give some segments of the video less compressing to frames that need more data and less to frames with less data. This can be controlled by the Minimum and Maximum values. The decoder bitstream buffer size. Multiplexing is the process of combining separate video and audio streams into a single file, similar to packing a video file and MP3 audio file in a zip-file.
Audio format to use. For each codec, you can control the bit rate quality of the sound in the movie. Higher bit rates are bigger files that stream worse but sound better. Use powers of 2 for compatibility. If a problem occurs while rendering, the file might become unplayable and you will have to re-render all frames from the beginning. Each spreadsheet can contain multiple worksheets. Therefore, the worksheet name is a required parameter. To complete the data set definition, we need to replace the correct sections of the connection with the correct parameters.
There are six more options to this format that I will let you discover. This file format can only be used as a source in a copy activity. Therefore, we can use a pipeline to copy the data from one format to another.
However, the quickest way to test the data set is to place a sample file in the correct file path. The image below shows the first three records of the file in a preview window. In a nutshell, I have been working with the Microsoft Excel format for over 30 years. It is a file format that will be found on-premises as well as in-cloud. The Extensible Markup Language XML is a file format that defines a set of rules for encoding documents in text file that is both human-readable and machine-readable.
XML is widely used in a Service-oriented architecture SOA in which disparate systems communicate with each other by exchanging messages. In a nutshell, XML has become a commonly used format for the interchange of data over the Internet.
Once again, the name of the folder and file should be parameterized. The image below shows the correct XML extension being added to the table name. Additionally, we can pick a compression type, encoding format and null value.
For this simple test, I am going to take the defaults. I leave this experiment as exercise you can complete. The image below shows the first two records of the file in a preview window.
Many popular products use the XML format. Thus, you can copy from these formats to another using the COPY activity. The binary format is even more restrictive. It allows the developer to copy from one linked service to another as long as the source and target are both binary data types. Why is this restriction so?
Binary files are governed by the source systems that generate them. The same number written to a file might have a different representation. The Binary connector for ADF does not allow the developer to know the internals of a given files.
It just guarantees that the data, byte by byte is copied from the source connection to the destination connection. Like before, we need to parameterize the directory path and file name for the data set to be truly dynamic.
Before saving the data set, please make sure you modify the connection properties to use the newly created parameters. The image below shows that the validation of a sample pipeline fails when we try to read from Azure SQL Server and write to Azure Data Lake Storage using a binary format.
That was a lot of work, to give you the reader, a complete background on all the data types that can be used with Azure Data Lake Storage. The " if condition " activity is a control flow object that can be use with ADF pipelines.
Today, we are going to revisit the prior pipeline to allow the source location to dynamically be defined by the pipeline parameters. Our first step is to come up with a list of parameters needed by the pipeline program. The image above shows the parameters that I choose to define.
The schema name and table name are used to uniquely identify the source table to copy over from the Adventure Works LT database. The zone name describes the quality zone within the data lake in which the pipeline is writing to. We are depending on the fact that the table name and file name will be the same to simplify and reduce the number of parameters. Also, the zone, schema, and table are used to uniquely define the full path to the file in the data lake.
The last three parameters are used to define the characteristics of the file: file type — what is the target data file type; file extension — might change when writing delimited files; and delimiter char — popular characters are comma, tab and pipe. This is a very good choice for a name since we are now supporting 5 different target file formats. Let us talk about how to create the correct "if condition" activity with an inner most copy activity. Once you understand how to write one, the rest of the activities are just repeating of the design pattern using a different target data set.
Name conventions and descriptions are very important for long term maintenance. Please enter the following ADF expression for the "if condition" activity for the delimited file format. Simply change the string comparison for each copy activity accordingly. There are three settings that most developers forget to set. The timeout for the activity is set to seven days by default. I have chosen to set this value to five minutes. The retry count and retry interval allow you to recover if the first copy command fails.
I left this at zero retries since it is a "proof of concept" POC framework. In real life, one might choose a setting of three or five retries with a several minutes between copies. Please configure the source setting of the copy activity by using the following ADF expression. In this POC pipeline, were are assuming that a full table copy is being used every time we move data. The image below shows no partitioning none being used to copy over the table data. I will visit topic in a follow up article.
Please configure the target sink setting of the copy activity by using the following three ADF expressions for directory name, file name and delimiter character. The image below shows the completed copy activity. Make sure you publish save your work now before you lose it. Please click the debug button after your finish creating all five "if condition" activities. It is interesting to note that all 5 if conditions must be evaluated which results in some lost processing time.
While this is not a lot of time, it can add up if the pipeline program is executed many times. The switch activity is part of the control flow objects that can be use with ADF pipelines. Can we use this conditional activity to reduce the wasted computing time see with the "if condition" activity?
Instead of 5 "if condition" activities, we have 1 switch activity. Please enter the following ADF expression for the switch statement. The only rule to follow is the evaluation of the expression must result in a string. There is a default activity that is called if no matches are found. Use the add case button to create an entry for each file type. This is the vertical subset of a relation. In this fragment overlapping columns can be seen but these columns are primary key and are hardly changed throughout the life cycle of the record.
Hence maintaining cost of this overlapping column is very least. In addition this column is required if we need to reconstruct the table or to pull the data from two fragments. Hence it still meets the conditions of fragmentation. This is the combination of horizontal as well as vertical fragmentation. This type of fragmentation will have horizontal fragmentation to have subset of data to be distributed over the DB, and vertical fragmentation to have subset of columns of the table.
As we observe in above diagram, this type of fragmentation can be done in any order. It does not have any particular order. It is solely based on the user requirement.
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