What do you understand by the term data lake? What does it mean? Data lakes are low-cost, scalable, and centralized data repositories for the storage of data from multiple resources. It allows the user to store raw data from various resources without any structuring or change. They can use it for gaining better insights and making good decisions.
Data lakes are gaining increased popularity in the business market. In 2019, the market value of data lake was calculated to be around $3.74 billion and it is predicted to be around $17.6 billion till 2025
What Is A Data Lake?
The ability to store all of your structured and unstructured data at any scale is provided by a data lake, which is a centralized repository. You may run several sorts of analytics, from dashboards and visualizations to big data processing, real-time analytics, and machine learning to help you make better decisions, without first structuring your data. This includes analytics such as dashboards and visualizations.
What Is The Need For A Data Lake?
Businesses that successfully extract commercial value from their data will outperform their competitors. According to a study by Aberdeen, businesses who have used data lakes outperform comparable businesses in terms of organic revenue growth by 9%. The data lake’s storage of log files, click-stream data, social media data, and data from internet-connected devices allowed these leaders to perform novel sorts of analytics, such as machine learning, on these new sources. By attracting and keeping consumers, increasing efficiency, keeping equipment up to date, and making wise judgments, this enabled them to see and seize business growth possibilities more quickly.
The Challenges Of Building Data Lakes
Data lakes are very beneficial and advantageous but building them is not an easy task. It is a lengthy and time taking process. It requires several manual steps. These manual steps are quite complicated and time-consuming. It requires the loading of data from various resources so it is quite hectic. The monitoring and controlling of data flow also require a lot of effort. You need to do the de-duplication of the duplicated and excess data. You are required to turn on encryption and set up boundaries or partitions.
It is the most difficult task to select the right quality of data, proper architecture, adequate technology, and proper data governance for data lakes. Without proper measures, a data lake can easily convert to a data swamp or an isolated pool having inaccessible, hard-to-use, and difficult-to-understand data.
These problems can be solved and things can be simplified by using the cloud — the AWS Cloud or modern data lake.
The Case For An AWS Cloud-Based Data Lake
AWS cloud-based data lake is cost-effective with lesser hassle required for the management of things. As compared to traditional data lakes, cloud-based data lakes have lowered engineering costs and increased efficiency ratios because of cloud-based tools. Cloud servers are more flexible and they are famous for offering on-demand infrastructure.
The data lakes that are AWS cloud-based have more benefits as compared to the simpler ones as the AWS cloud offers more benefits due to its large service portfolio. This wide portfolio of services allows the building and analysis of data lakes.
You can take benefit from the AI services of AWS for getting a better insight into unstructured datasets. These AI services are Amazon Comprehend, Forecast, Personalize, and Rekognition. AWS Glue makes it easy for users to find the relevant data and use it for analysis.
AWS Lake Formation Addresses The Trends Of Building Data Lakes
There are many services provided by 0scale, one of them is AWS Lake Formation. AWS Lake Formation simplifies the building process of the data lake. It also addresses the trends of building and using data lake.
1. The Need For Data Preparation
The generated data generally, is mostly in a humongous quantity and this amount is increasing day by day. Most of the generated data is usually semi-structured or even unstructured as this is raw data coming from numerous sources in various forms and types. Such type of data collected from a wide variety of sources is messy, unstandardized, and inconsistent. Such type of data requires transformation and cleaning before analysis. This is a very effort-requiring and tiring process.
Lake Formation features are facilitating the process of data preparation. It uses ML for cleaning the messy data and de-duplicating the redundant data. This helps in the improvement of data quality and increases consistency. Furthermore, it uses FindMatches for matching the records of different datasets with almost zero human intervention.
2. Data Lake Automation
The automation of data lakes requires several manual steps. These manual steps are quite complicated and time-consuming. It requires the loading of data from various resources so it is quite hectic. To make a beneficial data lake, you have to go through a lot of manual steps. Data lake automation requires a lot of steps, the more the merrier.
Data Lake Formation helps in the process of automation by employing pre-defined templates. These pre-defined templates enable the ingestion of data from various sources. Then it automates the configuration of storage. It then partitions the data automatically and transforms it into ORC or Apache Parquet formats. Lastly, it does the classification and preparation of data.
3. Greater Cost-Effectiveness
As we all know that in the cloud, the users are offered greater cost-effectiveness as compared to the simple data lakes. With the increased performance, it is quite difficult to keep the price low. Because mostly the cost and performance increase is taken simultaneously. This is a big problem for organizations and companies because they have to look after both performance and cost.
Lake Formation also solves this problem to a higher extent. This is a case in which we can take help from Lake Formation. Both the cloud and Lake Formation can work in collaboration to solve the cost-effectiveness issue. Lake Formation reduces the overhead and increases the efficiency of the data lakes. It allows the efficient partitioning of data collected from a variety of sources. It improves the performance and organizes data with a reduction in the overall cost.
Furthermore, the features of the Lake Formation are not having any extra charges over them. It integrates itself with many services for providing better performance. These services can be AWS IAM, Amazon Athena, Amazon CloudTrail, Amazon EMR, Amazon Redshift, Amazon CloudWatch, etc.
4. Accommodating More Data And More Diverse Workloads
When we talk about data lakes, the generated data is mostly in a humongous quantity and this amount is increasing day by day. Most of the generated data is usually semi-structured or even unstructured as this is raw data coming from numerous sources in various forms and types. Both the sources and amount of data are increasing day by day. It gets very difficult for the data lakes to accommodate and handle that huge amount of data.
So once again, here comes the role of the Lake Formation. It imports data from AWS databases, consisting of Postgres, MySQL, MariaDB, SQL Server, and Oracle databases. They support both incremental and bulk data loading. Lake Formation eases the processes of accommodating data and diverse workloads. It is not an easy task and requires the employment of the correct set of strategies. As we talked about earlier, the data is collected and gathered from diverse sources so the data is semi-structured and mostly unstructured. Lake Formation helps the data lakes in managing and accommodating data and workloads.
5. The Balance Between Data Governance And Ease Of Use
From the user’s perspective, the most important feature of data lakes is that they must be accessible with ease. They must maintain a balance between governance and ease of use. They are supposed to be well-governed and secure. This combination of data governance and ease of use may not seem synonymous but it is a must for a perfect infrastructure of data lakes.
Lake Formation also works in this area of concern. This helps the data lakes by augmenting AWS Identity and Access Management (IAM) policies. AWS sends the credentials of people to the Lake Formation. Which then permits access of data to the data lake. Lake Formation offers its users policies that allow their access to the data with ease. The IAM integration enables the use of Microsoft Active Directory or LDAP to federate into IAM using SAML.
Data Lakes In Action
As compared to traditional data lakes, cloud-based data lakes have lowered engineering costs and increased efficiency ratios because of cloud-based tools. Cloud servers are more flexible and they are famous for offering on-demand infrastructure. They have to do automation, accommodation, the balance between governance and ease of use, and cost-effectiveness.
0scale is known for its vast experience when it comes to building data on the AWS cloud. As we know that every customer is different so as the needs. Every customer has different needs and requirements. Our prime task is to build data lakes that cater to the customers’ needs.
0scale can provide you with guidance that whether a data lake is suitable for your organization or not. Because it depends on your company and data lakes cannot always be the possible solution for you.
So this guide was all about data lakes and their benefits that make them desirable and valuable for businesses and organizations. We also discussed what are AWS cloud-based data lakes and their operation. We discussed how the Lake Formation helps the processing and building of data lakes.
So if you are confused, feel free to contact us in case of any queries and questions. For further details about Lake Formation, you can the AWS website.
