Data is at the forefront of business development driving the transformation with many insights from the data analysis. The numerous analytical processes have been able to unearth exciting discoveries that help in making better business decisions. As the amount of data around the world is on constant rise, the ability to handle such data is becoming exceedingly difficult.
The Challenges with Data Lake
With data becoming an integral part of the business growth, no company can do without data. Hence, many resorts to various techniques to capture these data from different sources and store them for analysis. These vast volumes of data form the data lake. The data from multiple sources are stored in its native format in the data lake. So, the question is, even if the businesses have managed to find solutions to create a data lake, how can one be able to process this big data for insights?
This raw data from the data lake is useless unless it is analysed. Hence, a data lake architecture that has robust security features to keep the data safe is just the start of the data handling process. Companies require similar data analytical architecture that can convert these data from their native form to a usable format that can be fed to analytical tools.
Furthermore, the data analytical tools should be able to read the data directly from the data lake. Else, one has to copy the data from the data lake and load it into the analytical software which will consume an enormous amount of power and waste multiple resources. If companies use a data lake that isn’t compatible with a lot of vital data analytical tools, then it could limit their data handling capacities which will directly cripple the results of the analysis.
It is essential to provide complete freedom to those handling the data with access to all the top data analytical features to get the maximum amount of insights from it. When you shorten the options to choose from, it automatically takes the toll on the result of the analysis which, in the end, makes it a waste after going through such tremendous efforts to collect and process the data.
As a solution to all such data handling difficulties, Cloudera’s Enterprise Data Hub (EDH) helps in all the aspects of handling data right from the collection of the data lake, gathering the data and converting them into the required format and processing them for insights.
Cloudera’s EDH – The All-in-One Solution for Data
Hadoop has been one of the most common file systems used by many organizations around the world for storing the data. Hadoop Distributed File System (HDFS) provides a scalable platform where a massive amount of data can be stored under high security and accessed from anywhere. The data from any format can be captured and store in the HDFS for future analysis. It is also easy to filter the data from the HDFS to process for specific applications.
If you already have a large amount of data in the Cloudera EDH platform, it can access the data from the HDFS system directly and seamlessly without any issues. If not, you can use the EDH as a data warehouse to store, process and keep the data ready for analytics.
Cloudera’s EDH platform is a combination of multiple tools present in a single platform. You can use it for data warehousing, data engineering, data operations, real-time data analytics, data reporting and everything else you need to do with the data.
Combating the Data Analytical Complications
You can use either a hybrid cloud environment or a multi-cloud environment, or a combination of both with the Cloudera’s EDH platform. You can load and stream the data directly to the platform for easy lifecycle management. You can sort the data in flexible data silos and give the freedom to the data scientists to work with multiple tools of Apache HBase, Apache Spark, Apache Impala, Apache Hive, Apache Pig and a lot more resources from Apache and Cloudera.
By using the Cloudera EDH platform, you can eliminate most of the operational difficulties that come with the system. The data can be managed through a centralized platform accessible to all. This also hugely helps with real-time analytics where the issues of delays in moving the data from the storage are avoided.
Cloudera also provides a Shared Data Experience (SDX) that helps to manage complex data challenges by bringing together different data analysis tools and simulated conditions to get the closest analysis as possible. It is a self-managed framework for cloud applications that are best used to collaborate and work on any data complications.
With all such combinations of various features that directly answers the data management and analytical issues, Cloudera’s Enterprise Big Data Solutions can make it much easier and faster to process and implement data-driven decisions. You can save much money on operational costs and have a focussed data analytical process that cuts down the unnecessary clutter through the EDH platform.