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This article introduces the relevant knowledge of "how to connect with SQL Server Hadoop". Many people will encounter such a dilemma in the operation of actual cases, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Apache Hadoop cluster

Hadoop is a master-slave architecture deployed in a cluster of Linux hosts. To deal with large amounts of data, the Hadoop environment must include the following components:

The master node manages the slave node, which mainly involves processing, managing and accessing data files. When the external application sends a job request to the Hadoop environment, the primary node also acts as the primary access point.

The named node runs the NameNode daemon, manages the namespace of the Hadoop distributed File system (HDFS) and controls access to data files. The node supports the following operations, such as opening, closing, renaming, and defining how blocks are mapped. In a small environment, a named node can be deployed on the same server as the primary node.

Each slave node runs the DataNode daemon, which manages the storage of data files and handles read and write requests for files. The slave node consists of standard hardware, which is relatively cheap and available at any time. You can run parallel operations on thousands of computers.

The following figure shows the relationship between the various components in the Hadoop environment. Notice that the master node runs the JobTracker program and each slave node runs the TaskTracker program. JobTracker is used to process requests from client applications and assign them to different TaskTracker instances. When it receives instructions from JobTracker, TaskTracker runs the assigned tasks with the DataNode program and handles data movement during each operation phase.

You must deploy the SQL Server Hadoop connector within the Hadoop cluster

MapReduce framework

As shown in the figure above, the master node supports the MapReduce framework, a technology that relies on the Hadoop environment. In fact, you can think of Hadoop as a MapReduce framework in which JobTracker and TaskTracker play key roles.

MapReduce breaks up large data sets into small, manageable data blocks and distributes them among thousands of hosts. It also includes a series of mechanisms that can be used to run a large number of parallel operations, search for PB-level data, manage complex client requests, and analyze the data in depth. In addition, MapReduce provides load balancing and fault tolerance to ensure that operations can be completed quickly and accurately.

The MapReduce and HDFS schemas are tightly integrated, and the latter stores each file as a sequence of blocks. Data blocks are replicated across clusters, and all data blocks in the file are of the same size except those of *. Each slave node's DataNode program works with HDFS to create, delete, and copy data blocks. However, a HDFS file can only be written once.

SQL Server Hadoop connector

The user needs to deploy the SQL Server Hadoop connector to the primary node of the Hadoop cluster. The master node also needs to install Sqoop and Microsoft's Java database connection driver. Sqoop is an open source command-line tool for importing data from relational databases, transforming it using the Hadoop MapReduce framework, and then importing the data back into the database.

When the SQL Server Hadoop connector is deployed, you can use Sqoop to import and export SQL Server data. Note that Sqoop and connectors operate in a centralized view of Hadoop, which means that when you import data using Sqoop, you retrieve data from the SQL Server database and add it to the Hadoop environment, whereas exporting data refers to retrieving data from Hadoop and sending it to the SQL Server database.

Sqoop imported and exported data supports some storage types:

Text file: basic text file, separated by commas, etc.

Sequence files: binary files that contain serialized record data

Hive tables: tables in the Hive data warehouse, which is a special data warehouse architecture built for Hadoop.

Generally speaking, SQL Server and Hadoop environments (MapReduce and HDFS) enable users to deal with large amounts of unstructured data and integrate them into a structured environment for report making and BI analysis.

Microsoft big data's strategy has just begun.

SQL Server Hadoop connector is an important step on the road of Microsoft big data. But at the same time, because Hadoop, Linux and Sqoop are all open source technologies, this means that Microsoft has to open its mind to the open source world on a large scale. In fact, Microsoft's plan is more than that. by the end of this year, they will launch a solution similar to Hadoop and run on the Windows Azure cloud platform as a service.

Next year, Microsoft plans to launch similar services for the Windows Server platform. There is no denying that the SQL Server Hadoop connector is of great significance to Microsoft. Users can deal with big data's challenge in the SQL Server environment. I believe they will bring us more surprises in the future.

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