Here’s how to save time and effort with cloud data tools

 Data transformation is at the heart of every consumer-centric business today. Small, medium or large, companies are harnessing the power of raw data to generate useful insights and make better business decisions. But to make the most of the data available at hand, businesses need to rely on best practices and flexible solutions.

To enable data transformation at scale, businesses need to move and access data in real-time, and that starts with the bulk loading of files. Compared to traditional data loading which is time and resource consuming, loading data in bulk is speedier and more secure, especially for businesses that have a massive volume of information to store, access and process.

Bulk loading enables data operators and administrators to load large volumes of data in a more efficient and streamlined way. By saving time on manually entering and uploading files one at a time, bulk loading helps in parallel data uploading using multiple nodes to speed up the process.

Further, uploading thousands and thousands of records and files one at a time is also affected by LAN/WLAN speed issues, therefore, making it more susceptible to unwanted issues and downtime.

By choosing the bulk loading method, businesses need to execute the data uploading query only once, albeit using the right parameters, and they can leverage a faster, more optimized outcome.

Comments

Post a Comment

Popular posts from this blog

The Functioning of Data Lake Built on Amazon S3

Image
 The Simple Storage Service (S3) of Amazon is a cloud-based data storage service that stores data in its native format – unstructured, semi-structured, or structured. Here, metadata and objects in data files are stored in buckets. For uploading files or metadata, the objects have to be first moved to Amazon S3. After this step is completed, permissions can be set on the metadata or the related objects stored in the buckets (containers) and selected personnel are given access to them. They are given the authority to decide where the logs and objects will be stored on Amazon S3. Various competencies can be used for building an S3 data lake on Amazon S3. These include media data processing applications, Artificial Intelligence (AI), Machine Learning (ML), big data analytics, and high-performance computing (HPC). All these used in tandem give organizations access to critical business intelligence and analytics as well as unstructured data sets from S3 data lake. There are several bene...
Read more

Bulk Insert for Microsoft SQL Server Table

Image
 Why should you use Bulk Insert to copy massive volumes of data into a Microsoft SQL table or view? It is because the process is fast, efficient, and secure. An example will better explain the Bulk Insert task. Suppose your business has million-row product types stored on a mainframe. However, to populate web pages, your e-commerce system uses SQL Server, and every night you have to update the SQL Server table with the master product list located in the mainframe. To make the task of updating the table very easy and quick, you can save the product list in a tab-delimited format and use the Bulk Insert task to copy the data directly to the SQL Server table. To make sure that the high-speed copying is not disrupted, you should refrain from performing transformations on the data while it is moving from the source file to the table or view. Considerations before taking up a Bulk Insert task •Data only from a text file can be transferred into a SQL Server table or view by the Bulk Inse...
Read more

SAP Data Lake – Evolution and Architecture

Image
 April 2020 saw the launch of the SAP HANA Data Lake designed to further increase the capabilities of SAP of its data storage capabilities. The goal was to provide storage options to customers at very affordable rates. The package has SAP HANA native storage extension as well as the SAP data lake incorporated in it. This cloud-based relational data lake of the SAP IQ ecosystem has features at par with the leaders in this field namely Microsoft Azure or Amazon Simple Storage Service (S3). Architecture of the SAP Data Lake The unique SAP data lake architecture resembles a pyramid with the top, middle, and bottom segments having specific storage capabilities. At the top section of the structure is all data that is critical for organizations and hence the cost of data storage here is the highest in the SAP data lake. This data is frequently accessed and processed for operational requirements. The middle of the pyramid stores data that is not accessed frequently but not insignificant e...
Read more