The primary objective in this data-rich era is to extract the insights of data that can be used for making outcome-oriented decisions. Understanding how to analyze data is a skill that is in demand. We are creating hundreds of megabytes of data every day, and only a nominal value is getting analyzed to the improvement of various processes. Although that nominal value is not so nominal because that data is in a tremendous amount, and we have a hard time handling that.
In today's world, technologies are continuously evolving, which has brought a revolution in our lives. Especially cloud technology has completely changed the business industry. Now that we have an option of cloud data warehouses, it has become relatively easier to analyze data. Organizations are using these warehouses to store their data and processing it at the same time. There has been an evolved version of databases that can store different types of data in a very huge number. This evolved version is called data lake, and we can store structured, non-structured, and semi-structured data of any amount in it.
Data Lakes, The Latest Data Storage Solution
In data warehouses, we can store different types of data as we can define a schema in those warehouses and they can store some known data types, but when there is an unknown data type, it is going to leave that data behind. As the world is getting faster, organizations are leaving all the traditional methods of data storage and moving towards the latest storage solution to keep up with the pace. Data lakes have become the center of focus for organizations towards the latest and evolved storage solution.
Since it is hard to store unknown data in the data warehouses, let alone the analyzing and processing of that data, data lake has come up to be the best option for data warehouses to store all kinds of data and to process them and extract the useful insights as well. A data lake is an option that you can trust for your business, as it takes care of your application and software system in the form of data in a single repository. It is a low-cost data storage option that is very effective, and it catches the attention of consumers as well.
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On-prem Data Lakes and Open-Source Software
It was a decade ago when companies started opting for Apache Hadoop to build their data lakes as it was a cost-effective option at that time. And there was not this much awareness about the true potential of data lakes as well. There used to be a drawback of using Apache Hadoop for the data lake. It was a very time-consuming process to integrate data sources with Hadoop, plus it used to be available for data processing only on Hadoop. This is the processing which cannot be suitable for every application, especially the ones with real-time data processing. It also had the potential to become less valuable and passive very quickly.
It is since the last few years that we are seeing a new architecture for capturing the flow of streaming data. This architecture is called Kafka and we can use it to store, read, and analyze all kinds of streaming data. It is open-source software, and we can use it for free, and being open-source also means that a lot of big developers contribute to making it better, that is the reason we keep having updates of the software. This architecture works in a distributed manner, which means it does not stop on a system and keeps moving from server to server to give you a better speed of processing. Real-time data analysis is the thing for today, as by doing this, companies stay competitive in the market, and Kafka is the best option for that right now. To know data lake more, deeply you can go for Google cloud certification, as it has thorough knowledge about data lake.
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Data Lake Building and Management
In the modern world today, every data lake solution is based on Apache Kafka or a service that is fully managed by Kafka. There are two benefits if that, first one is the ease of processing and storing every type of data, and the second one is that it lets you use the full potential of data even when you are migrating from an on-premises data lake to the cloud. Organizations nowadays are moving from on-premises data lake to a fully managed cloud solution, and there are reasons for that. Cost efficiency, data security, consistency, strong performance are some of those reasons. With those benefits, data lakes help you take full advantage of other platforms that include AI platform, which enables you to take a better insight from streaming and batch data. We can use Confluent Replicator for data migration, and all of our data can be shifted to another Kafka cluster. We can also do data ingestion by using Kafka or Confluent as we want. Management of data has become easier now, and it is just because of Apache Kafka. We can copy a topic with the same configuration and message to another cluster of Kafka within no time. We can move data not only from Hadoop data lake but from oracle, Netezza, Postgres, and other on-premises data stores.
It is all about migrating the data lake, once this process is done, now the focus turns towards analyzing the data in the best possible way. And Kafka is the most beneficial data lake architecture by now that stores your data in a well-defined manner, as soon as we ingest the data. Organizations are indeed moving towards cloud solutions and leaving traditional on-premises data lake solutions, and for that, they are using Apache Kafka. As it is the best data migration and management option right now with benefits like high performance regardless of the data size, fault tolerance, and very good durability. That is all from us. Thank you for your time.
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