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Showing posts from January, 2022

The Intricacies of AWS CDC to Amazon Simple Storage Service

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  Let’s see the many intricacies of the Amazon Web Service Change Data Capture (AWS CDC) feature while building data lakes on the Amazon Simple Storage Service (S3). When AWS CDC to S3   is carried out from a relational database that is located upstream to a data lake on S3, it is necessary to handle the data at a record level. The processing engine has to read all files, make the required changes, and complete datasets. Change data capture rewrites the files as new activities such as all inserts, updates, and deletes, in specific records from a dataset. On the other hand, poor query performance is often the result of AWS CDC to S3 . It is because when data is made available by AWS CDC to S3   in real-time, it becomes split over many small files. This problem is resolved with Apache Hudi, an advanced open-source management framework. It helps in managing data at the record level in Amazon S3, leading to the simplified creation of CDC pipelines with AWS CDC to S3 . Data ingestion is
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Enhancing Database Performance with Real Time Replication

  The process of copying and distributing database objects from one database to another is called Replication. The location of the source and the target databases is irrelevant here is the activity is done over wireless connections, local and wide area networks, and the Internet. In the past, organizations that were heavily dependent on data had to manage with several users working on a single server leading to inefficiencies as well as maintenance issues. These were solved with the launch of the real time replication   process that provided database copies to users working even in multiple remote locations. This greatly helped to increase database performance as the databases could be located close to the users. There are several cutting-edge advantages of the real time replication   technology. The most important is that critical data from multiple sources can be integrated and loaded to data warehouses or replicated to cloud storage for distribution to databases in remote location
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How Data Preparation on AWS Increase Business Efficiencies

  The current business scenario is mainly data driven with massive volumes of data. The handling of a large number of applications, data, and tools require using advanced algorithms, models, and machine learning. To this end, there are several solutions available in the AWS Marketplace that provide users with the flexibility of selecting from a wide range of pre-built models and algorithms that are perfect across industries and use cases. Apart from Machine Learning (ML), AWS also offers Artificial Intelligence (AI) platforms. They help to simplify the experimentation of data for formulating deep insights from different sources across the data environment. However, to get the most out of these tools it is essential to opt for data preparation on AWS. What is data preparation? Machine Learning models are only as good as the quality of the data that is used and hence it is essential that suitable training data is maximized for learning. This is data preparation and includes data pr
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The ETL Process and the Tools Used For AWS

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A popular method of data collection from multiple sources and uploading the data to a centralized data warehouse is the ETL process. This Extract, Transform, Load activity is a three-step task. The first is extracting the information from sources like databases, followed by converting the files and tables so as to match the specific data warehouse architecture and finally, loading them into the data warehouse.  Click here to know more. Amazon Web Service (AWS) is a cloud-based computing platform with payments in proportion to the quantum of computing and storage resources used. All the cutting-edge advantages of a cloud environment like unlimited storage options, instant server availability, and effective handling of work are inherent in AWS.  Click here to know more. Now, what are the features that should be in-built into the best ETL tool for AWS? • A good ETL tool should be user-friendly and must integrate easily with the existing structure. • Easy management and monitoring with th
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Using Microsoft tools for an effective migration

 In the current time, there is hardly any IT professional who is not aware of the fact that an Oracle Database is not only a very good database solution, but is also very reliable and efficient. But the only problem is that it is quite expensive. In case you wish partition a table in Oracle, it’s an option that you will have to shell out a huge amount of money for and is similar to some other advanced security options Dynamic Data Masking and Database Encryption. On the other hand, an SQL Server comes loaded with similar options out of the box and is equally if not better option when compared to Oracle database.   One can observe and clearly state that a very wide number of applications all around the globe are choosing to migrate to Microsoft SQL Server from Oracle Database. It is probably the biggest reason why Microsoft is helping its customers by developing tools that provide assistance throughout the migration whether it is AWS DMS Oracle to S3 or simply data migration to AWS. All
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