4 Biggest Mistakes you should avoid on Data Lake Implementation

Data Lake solutions has consistently been a test for companies, yet putting away it in a way that is promptly available and helpful has demonstrated to be significantly all the more confusing. Enter "data lake," a much-hummed about answer for associations who need a superior method to store and work with mass measures of information and investigation. 

Data lakes as  a services, and enormous information advances like Hadoop, HDFS, Hive and HBase, have immediately developed in fame due to their capacity to have crude information from applications in all structures, regularly at a little expense than big data solutions stockrooms. 

The thought is that associations can then effectively look for the data they need, paying little mind to source or arrangement, helping them influence investigation all the more viably in their everyday business tasks. 

In any case, data lakes solution additionally offer a prime open door that such a large number of associations are missing – the capacity to adapt their data. 

1. An excess of Hadoop: When Hadoop conveyances or bunches spring up all over undertakings, there is a decent possibility you're putting away heaps of copied information. This makes data warehouse, which restrains big data services since representatives can't perform far reaching examinations utilizing the entirety of the information. 

2. A lot of administration: Some associations take the idea of administration excessively far by building an data lake solutions with such huge numbers of confinements on who can view, access, and work on the information that nobody winds up having the option to get to the lake, rendering the data storage solutions.

3. Insufficient administration: Conversely, a few associations need more administration over their data lake as a services, which means they need legitimate information stewards, devices, and arrangements to oversee access to the information. 

The information can become "dirty" or "altered," and in the long run the business quits confiding in the information, once more, rendering the whole data lake solutions. 

4. Inelastic design: The most widely recognized slip-up associations botch is building their information lakes with inelastic engineering. 

Since information stockpiling can be exorbitant, associations regularly gradually and naturally develop their big data solutions condition each server in turn, frequently beginning with essential servers yet in the long run adding elite servers to stay aware of the requests of the business. 

There haven't been any prescribed procedures or philosophies set up to assist associations with characterizing the potential estimation of their data so they can put resources into the capacity and investigative innovations they have to accomplish this future. 

Conclusion

Similarly as with any developing innovation, it will require some investment before data lake solutions, and in this manner the associations who run them, have arrived at their maximum capacity. Yet, the individuals who can begin the voyage now – deliberately and with a long haul vision – remain to make a gigantic aggressive lead that will be hard to decrease in the years to come.

Thanks and Regards,

Grace Sophia

Indium Software - Data Lake Solutions | Data Lake As a Services

4 Secrets Will Make Your Data Lake to be Amazing

A data lake solutions is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake as a services is usually a single store of all enterprise data including raw copies of source system data and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video)

The Difference between Data Lake Solutions and Data Warehouse Solutions

A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. The two types of data storage are often confused, but are much more different than they are alike. While a data lake solutions works for one company, a data warehouse will be a better fit for another.

Benefits of a Data Lake Solutions

• Ability to derive value from unlimited types of data
• Ability to store all types of structured and unstructured data in a data lake
• More flexibility
• Ability to store raw data
• Democratized access to data via a single

Various sectors in which Data Lake services can be implemented

• Oil and Gas
• Big Government
• Life Sciences
• Cyber security
• Marketing and Customer Data Platforms

Ok, lets discuss with 4 secrets which would make your Data Lake solutions to be amazing

#1. Don’t replace, enterprise data warehouses and data marts

The modern data lake solutions is great for enriching large data sets and correlating data sets that were previously spread across disparate sources. Technologies like Apache Hadoop are ideal for these huge environments because they offer lower costs around storage and processing.

The truth is, you will still find value in your data warehouses for specific types of queries and analytics, so you want to make sure you still retain the best tool

#2. Visual analytics make the big picture accessible

Enterprise data lake services users must be able to get insights without having to code. Otherwise, data lakes are just a private area reserved for technical teams.

To make data analytics as accessible as possible to the larger business analyst community, enterprises must invest in a tool that permits them to visually display that information, ensuring a data lake services isn’t a black box to less tech-savvy users. This feature enables non-techies to drill down into data and derive insights, and even make predictions, through an intuitive interface.

#3. Create a strong data culture

What is the use of having powerful visualize data if it can’t be shared? All businesses employing a data lake solutions need to create a governance framework that enables collaboration.

By creating a framework that allows shareable data sets and dashboards, everyone in the enterprise will be able to offer feedback on which models generate the most valuable insights.

#4. Have unified security and governance

It’s great to have shareable data lake solutions, but it has to stay in the right hands. This topic becomes even more critical as more stringent regulations require more controls in the enterprise.

Businesses must know what kind of data they have, where their sensitive data resides, how to handle it, and how to see it.

Conclusion

While data lake as a services have tremendous potential, they are not silver bullets. Organizations need to set themselves up for success by accompanying their data lakes solutions with apt technologies so they make their data visual, accessible, shareable, secure, and scalable. Hope you enjoyed this read, so that i need a feedback from you people.

Thanks and Regards,

Grace Sophia

Indium Software - Data Lake Solutions | Data Lake As a Services

Modern Strategies & Approaches of Data Lake Solutions

The data lake has earned a high reputation in the past few years as it boasts of a modern design pattern which is capable of fitting into the data of today. A wide assortment of people looks for different options for the organization and usage of data. For instance, there are a bunch of users who look forward to ingesting data into the lake in no time and thus it is available for analytics and operations immediately. They are willing to store the data in the original raw state so that they are capable of processing it in various ways with the evolution in the operations as well as business analytics.

They require capturing unstructured data, big data as well as data from various resources like customer channels, social media, Internet of Things, as well as external resources like data aggregators, partners, in a single pool. In addition to this, users are frequently under the pressure for developing the value of business as well as reaping the organizational benefits from the specific collection of data with the aid of discovery oriented analytics. Here is a list of few of the modern strategies as well as approaches for data lake solutions:

A data lake is known to be deployed on top of Hadoop for providing assistance with the different requirements and trends, in case the user gains success in resolving the challenges of Data Lake. The data lake is considered to be really new. Hence, the design patterns as well as best practices are considered to be really coalescing. Data Lake as a service has introduced high popularity in introducing the required methodology in Hadoop.

On-boarding as well as ingestion of data with no or little front improvement

The early ingestion as well as late processing is considered to be one of the most popular innovations of the data lake companies. It has specific similarities to Extract, Load, Transform or ELT. As you opt for the methodology of early ingestion as well as late processing, it enables the availability of integrated data for analytics, reporting, and operations. This demand for diverse ingestion processes for handling a wide array of interfaces, data structure as well as container type for the scaling of bulk data in addition to real time latencies. It is also useful in the simplification of on-boarding of latest data sets as well as data resources.

Also Read: Data Warehousing – Traditional vs Cloud!

Controlling who will be loading the data into the lake and the way in which it will be loaded

There are high chances that the data lake may transform into a undocumented and disorganized set of data in the data swamp which is challenging to leverage, govern as well as navigate without the proper control. Hence, it is a prerequisite to establish the right control with the aid of data governance, which is based on policy. The data curator should work for enforcing the anti dumping policies of the data lake services. The policies should be enabling the expectations as the data analyst dumps the data in the sandboxes of the analytics. You should be performing the documentation of data after its entry into the lake with the aid of Metadata, business glossary, information catalogue as well as different semantics and thus the users will be capable of finding the data, governing the data, optimization of the queries, as well as reduction of data redundancy.

Persistence of raw data for the preservation of original details as well as the schema

The detailed source data is known to be preserved in the storage so that it is possible to repurpose it repeatedly with the emergence of new requirements of the business for the Data lake solutions. In addition to this, raw data is considered to be an ideal option for discovery oriented analytics and exploration which functions with detailed data, larger samples as well as data nationalities.

As the end user is seen to work with the data lake over the due course of time, they break the rules for the application of standardization of light data which is essential for accomplished customer view, reporting, generalized exploration of data and recurring the queries.

Improvement of real time data during the processing and accessing of Data Lake solutions

It is considered to be common with the user practices of self service, viz., data exploration as well as discovery which is coupled with the visualization and preparation of data. The data is known to be standardized as well as modeled as it is found to be queries in the iterative form. During the exploration, the Meta data is also known to be developed. You should take into account that improvements in data should be application to the copies of data and thus you should ensure that the source of raw detailed data remains intact. In addition to this, users can bring an improvement in the data lake with the management of meta take, virtualization as well as different semantics.

Capturing big data along with other sources of data in the data lake

In accordance with survey data, more than half of the data lakes are deployed on the Hadoop exclusively with the other quarter which is deployed on traditional systems and Hadoop partially. There are wide assortments of data lake services which stand out of the ordinary in handling huge volume of big data as well as Web Data. Hence, Hadoop is considered to be a good fit. Data lakes, based on Hadoop stand out of the ordinary in capturing the collection of large data from a wide assortment of resources such as social media, IoT, marketing channels, etc.

Data lakes are not only for big data or for Internet of Things. There are wide arrays of users who combine the modern big data along with traditional enterprise data for enabling the advanced analytics. It is also effective in the extension of customer views along with big data. They also stand second to nine in enlarging the data samples of risk analytics, existing fraud as well as enriched cross source correlation for different insightful segments as well as clusters. If you’re making any drastic changes or improvements at your product or software, doesn’t it make sense to go with a company like Indium Software - Leading Data Lake Solution Provider.

Thanks and Regards,

Gracesophia