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9 Janvier 2021
In order to compress files/folders into a ZIP file or extract files from a ZIP archive in Windows 10, people tend to use software like WinZip or 7-Zip. Actually, Windows systems come with built-in Compressed Folder Tools so that users can compress and uncompress files/folders even without WinZip or any other outside zip tools installed. Now, this page will show you how to zip and unzip files in Windows 10 without WinZip software.
When unzipping files, you must look for malicious file paths, which can escape out of the directory you unzip into. This is known as a path traversal attack. The following example demonstrates how to check for malicious file paths and provides a safe way to unzip.
I will introduce three different ways to create a ZIP file in Windows 10.
Right-click on the file or folder that you want to compress into a ZIP file, choose Send to > Compressed (zipped) folder from the context menu.
Tips: If you want to ZIP multiple files/folders together, you need to individually click on the files/folders while holding on the Ctrl key on your keyboard to select all the files, right-click on any one selection, and then select Send to > Compressed (zipped) folder from the context menu.
Step 1: Open Windows 10 File Explorer. Locate the file(s) or folder(s) you want to zip and select it/them.
Step 2: Select the Share tab and then click on the Zip button. Compressed Folder Tools will compress the selected file(s) and folder(s) into a ZIP file in the same location. You can rename the ZIP file or accept the suggested name.
Step 1: Right-click on the Windows 10 desktop, select New > Compressed (zipped) Folder from the context menu to create a new zipped folder.
Step 2: Rename the new zipped folder or hit Enter to accept the suggested name.
Step 3: Drag the file(s) and folder(s) you want to zip into the new compressed (zipped) folder. Similarly, you can drag files or folders into any one existing zipped folder.
Step 1: Right-click on the .zip file you want to unzip, and then select Extract All from the context menu.
Step 2: In the 'Extract Compressed (Zipped) Folders' dialog, select one destination to save the extracted files, keep Show extracted files when complete checked, and then click Extract. Then it will display an extraction progress dialog box.
Step 3: When the progress is 100%, it will show you the extracted files in the folder you just specified in Step 2.
Above is how compressed folder tools help zip and unzip files in Windows 10 without using WinZip software. The tool is only for compression file of ZIP file form, but can't apply to RAR format. Learn more options to extract RAR file, see How to Unrar RAR Archive File with WinRAR/WinZIP/7-ZIP.
A common problem that organizations face is how to gather data from multiple sources, in multiple formats, and move it to one or more data stores. The destination may not be the same type of data store as the source, and often the format is different, or the data needs to be shaped or cleaned before loading it into its final destination.
Various tools, services, and processes have been developed over the years to help address these challenges. No matter the process used, there is a common need to coordinate the work and apply some level of data transformation within the data pipeline. The following sections highlight the common methods used to perform these tasks.
Extract, transform, and load (ETL) is a data pipeline used to collect data from various sources, transform the data according to business rules, and load it into a destination data store. The transformation work in ETL takes place in a specialized engine, and often involves using staging tables to temporarily hold data as it is being transformed and ultimately loaded to its destination.
The data transformation that takes place usually involves various operations, such as filtering, sorting, aggregating, joining data, cleaning data, deduplicating, and validating data.
Often, the three ETL phases are run in parallel to save time. For example, while data is being extracted, a transformation process could be working on data already received and prepare it for loading, and a loading process can begin working on the prepared data, rather than waiting for the entire extraction process to complete.
Relevant Azure service:
Other tools:
Extract, load, and transform (ELT) differs from ETL solely in where the transformation takes place. In the ELT pipeline, the transformation occurs in the target data store. Instead of using a separate transformation engine, the processing capabilities of the target data store are used to transform data. This simplifies the architecture by removing the transformation engine from the pipeline. Another benefit to this approach is that scaling the target data store also scales the ELT pipeline performance. However, ELT only works well when the target system is powerful enough to transform the data efficiently.
Typical use cases for ELT fall within the big data realm. For example, you might start by extracting all of the source data to flat files in scalable storage such as Hadoop distributed file system (HDFS) or Azure Data Lake Store. Technologies such as Spark, Hive, or PolyBase can then be used to query the source data. The key point with ELT is that the data store used to perform the transformation is the same data store where the data is ultimately consumed. This data store reads directly from the scalable storage, instead of loading the data into its own proprietary storage. This approach skips the data copy step present in ETL, which can be a time consuming operation for large data sets.
In practice, the target data store is a data warehouse using either a Hadoop cluster (using Hive or Spark) or a Azure Synapse Analytics. In general, a schema is overlaid on the flat file data at query time and stored as a table, enabling the data to be queried like any other table in the data store. These are referred to as external tables because the data does not reside in storage managed by the data store itself, but on some external scalable storage.
The data store only manages the schema of the data and applies the schema on read. For example, a Hadoop cluster using Hive would describe a Hive table where the data source is effectively a path to a set of files in HDFS. In Azure Synapse, PolyBase can achieve the same result — creating a table against data stored externally to the database itself. Once the source data is loaded, the data present in the external tables can be processed using the capabilities of the data store. In big data scenarios, this means the data store must be capable of massively parallel processing (MPP), which breaks the data into smaller chunks and distributes processing of the chunks across multiple machines in parallel.
The final phase of the ELT pipeline is typically to transform the source data into a final format that is more efficient for the types of queries that need to be supported. For example, the data may be partitioned. Also, ELT might use optimized storage formats like Parquet, which stores row-oriented data in a columnar fashion and provides optimized indexing.
Relevant Azure service:
Other tools:
In the context of data pipelines, the control flow ensures orderly processing of a set of tasks. To enforce the correct processing order of these tasks, precedence constraints are used. You can think of these constraints as connectors in a workflow diagram, as shown in the image below. Each task has an outcome, such as success, failure, or completion. Any subsequent task does not initiate processing until its predecessor has completed with one of these outcomes.
Control flows execute data flows as a task. In a data flow task, data is extracted from a source, transformed, or loaded into a data store. The output of one data flow task can be the input to the next data flow task, and data flows can run in parallel. Unlike control flows, you cannot add constraints between tasks in a data flow. You can, however, add a data viewer to observe the data as it is processed by each task.
In the diagram above, there are several tasks within the control flow, one of which is a data flow task. One of the tasks is nested within a container. Containers can be used to provide structure to tasks, providing a unit of work. One such example is for repeating elements within a collection, such as files in a folder or database statements.
Relevant Azure service:
Other tools:
The following reference architectures show end-to-end ELT pipelines on Azure:
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