Here in this blog, we will learn what is Amazon Redshift and Redshift spectrum along with the working of Amazon Redshift. This blog will also help you learn how to create a Redshift Cluster, and connect it to the database using the query editor.
Amazon Redshift एक अमेज़न वेब सर्विस है इस पोस्ट में हम इसके बारे में अच्छे से जानेंगे What is Amazon Redshift in Hindi की Amazon Redshift किसे कहते है।
Amazon Redshift is one of the many database solutions offered by Amazon Web Services which is most suited for business analytical workloads. Here in this blog on what is Amazon Redshift & Spectrum, we will learn what is Amazon Redshift and how it works. Further, we will also learn how to create a Redshift Database Cluster, then connect to the database using the query editor.
What is Amazon Redshift in Hindi?
Amazon Redshift is a data warehouse service which is fully managed by AWS. It is very simple and cost-effective because you can use your standard SQL and Business Intelligence tools to analyze huge amounts of data.
You can run complex queries against terabytes and petabytes of structured data and you will getting the results back is just a matter of seconds. The data is not needed to be converted to a particular file format, redshift accepts all types of data formats mentioned here – Avro, CSV, Grok, Ion, JSON, ORC, Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV.
Redshift spectrum is a feature which lets you run queries against exabytes of unstructured data which is stored in Amazon S3. No loading or ETL (Extract, transform, load) is required for the data. First AWS Redshift identifies the data which is local and which is stored in the S3 bucket. After that, it creates a plan to reduce the content on Amazon S3 that needs to be read. Then AWS Redshift Spectrum workers are called to read and process the data from Amazon S3.
AWS Redshift is a data warehousing solution from Amazon Web Services. Redshift shines in its ability to handle huge volumes of data — capable of processing structured and unstructured data in the range of exabytes (1018 bytes). However, the service can also be used for large-scale data migrations.
Similar to many other AWS services, it can be deployed with just a few clicks and provides a plethora of options to import data. Additionally, the data in Redshift is always encrypted for added security.
Redshift helps to gather valuable insights from a large amount of data. With the easy-to-use interface of AWS, you can start a new cluster in a couple of minutes, and you don’t have to worry about managing infrastructure.
Benefits of Amazon Redshift
Now that we understand what is Amazon Redshift and what is Amazon Redshift Spectrum, let move on and discuss the benefits that Amazon Redshift provides.The benefits of Amazon Aurora remain the same for both MySQL compatible and PostgreSQL compatible.
- Faster Performance
- Provides 10x times faster performance than the other warehouses
- You can set caching to increase the data retrieval speed.
- Easy to create, deploy, and manage
- You can create and deploy a warehouse in minutes.
- Most of the commons tasks are automated. Tasks that are automated are monitoring and managing your warehouse.
- Cost-effective
- There are upfront costs or contract periods. It is 10 times cheaper than a traditional data warehouse which is set up on-premises.
- Scalability at it’s best
- Query your data lake
- Redshift in AWS allows you to query your Amazon S3 data bucket or data lake. You can query petabytes of unstructured data using Redshift on Amazon S3.
- Highly secure
- Redshift in AWS lets you isolate your warehouse using AWS VPC
- You can create Customer Management Keys (CMKs) using AWS Key Management Service to encrypt your data in the warehouse
When Would You Want To Use Amazon Redshift?
Amazon Redshift is used when the data to be analyzed is humongous. The data has to be at least of a petabyte-scale (1015 bytes) for Redshift to be a viable solution. The MPP technology used by Redshift can be leveraged only at that scale. Beyond the size of data, there are some specific use cases that warrant its use.
Real-time analytics
Many companies need to make decisions based on real-time data and often need to implement solutions quickly too. Take Uber for example.
Based on historical and current data, Uber has to make decisions quickly. It has to decide surge pricing, where to send drivers, what route to take, expected traffic, and a whole host of data.
Thousands of such decisions have to be made every minute for a company like Uber with operations across the globe. The current stream of data and historical data has to be processed in order to make those decisions and ensure smooth operations. Such instances can use Redshift as the MPP technology to make accessing and processing data faster.
Combining multiple data sources
There are occasions where structured data, semi-structured data, and/or unstructured data have to be processed to gain insights. Traditional business intelligence tools lack the capability to handle the varied structures of data from different sources. Amazon Redshift is a potent tool in such use cases.
Business intelligence
The data of an organization needs to be handled by a lot of different people. All of them are not necessarily data scientists and will not be familiar with the programming tools used by engineers.
They can rely on detailed reports and information dashboards that have an easy-to-use interface. Highly functional dashboards and automatic report creation can be built using Redshift. It can be used with tools like Amazon Quicksight and also third-party tools created by AWS partners.
Log analysis
Behavior analytics is a powerful source for useful insights. Behavior analytics provide information on how a user uses an application, how they interact with it, the duration of use, their clicks, sensor data, and a plethora of other data.
The data can be collected from multiple sources — including a web application used on a desktop, mobile phone, or tablet — and can be aggregated and analyzed to gain insight into user behavior. This coalescing of complex datasets and computing data can be done using Redshift.
Redshift can also be used for traditional data warehousing. But solutions like the S3 data lake would likely be better suited for that. Redshift can be used to perform operations on data in S3, and save the output in S3 or Redshift.
Conclusion
इस पोस्ट में हमने Amazon Redshift अमेज़न वेब सर्विस के बारे में अच्छे से जाना। Hope this tutorial on what is Amazon Redshift helps. Check out the related posts to learn about more Services offered by AWS.
