Companies today collect tons of information, way more than ever before. This mountain of data is too much for old storage methods. Data storage has become increasingly complex, just like the companies themselves. Even how companies handle this data, from simple databases to giant warehouses, needs to be constantly improved to keep up.
This is where data lakes come into play. Let’s discover more about data lakes. This article will cover everything up.
Interested so far? Read our blog, “How to Implement a Successful BI Strategy,” to better understand Business Intelligence.
What are Data Lakes in Simple Terms?
A data lake, in simple terms, would be a centralized storehouse that is accessible to everyone and stores all kinds of data, structured or unstructured. There are two kinds of data lakes: private and shared data lakes. We will get into the depth of this later. However, the difference between the two is that one requires permission to access the data (Private), and the other is done through a collaboration (shared). The other kinds of data lakes can be either top secret, such as sensitive and governance data.
In simple terms, a data lake is like a central storage space where you can keep all types of data. You don’t have to reformat the data before storing it, and you can analyze it in various ways, such as processing big data, employing machine learning, creating data visualizations and dashboards, and conducting real-time analytics. This offers multiple options for making informed decisions.
Why Would You Use a Data Lake?
Organizations and small businesses that utilize data from data lakes to their advantage are known to outperform their competitors. A survey conducted by Aberdeen noted how those businesses that implemented a data lake resulted in 9% organic revenue growth.
These leaders were able to conduct advanced analytics, such as machine learning, on various new data sources, including log files, clickstream data, social media, and data from internet-connected devices stored in the data lake. This allowed them to quickly identify and take advantage of opportunities for business growth, such as attracting and retaining customers, increasing productivity, proactively maintaining devices, and making well-informed decisions.
Read more: How AI is Shaping the Business World.
What are the Basic Elements of Data Lake?
As large corporations create data lakes, a number of key capabilities need to be considered. Here, we will look at some of the data lake and analytical platforms:
Data Movement
Through data lake, you can import and access real-time data. The data is collected from many sources and is shifted into the data lake in its original format. This process allows you to access data of any size and save time while defining data structure, transformations, and schema.
Analytics
With data lakes, you can take several roles in your organization, such as developers, business analysts, and data scientists, to access data with their frameworks and analytical tools. Open-source frameworks, such as Presto, Apache Spark, and Apache Hadoop, are included for commercial offerings from data storehouses, business intelligence experts, and vendors. Data lakes enable you to run analytics without shifting your data to a different analytics system.
Catalog Data and Security
Data lakes enable access to store both relational and non-relational data. This includes data from business applications, operational databases, mobile apps, social media, and IoT devices. They also provide cataloging, indexing, and crawling capabilities to understand the data within the lake. Finally, ensuring that the data is secure and well-maintained is important to protect your data assets.
Machine Learning (ML)
Data lakes will allow firms to generate various types of insights, including reports on machine learning and historical data. Models are built to forecast and foretell likely outcomes and suggest various prescribed actions to achieve the best results.
Read more: 10 Hidden Areas Holding Back Your BI Data Strategy.
What is a Data Lake vs. Data Warehouse?
The comparison between a data lake and a data warehouse can vary depending on the requirements. While typically, an organization would require the needs of both, they both serve different needs and promote different cases.
While a data warehouse is primarily a database optimized to examine relational data coming from transactional systems and line-of-business applications, the structure of that data and the schema are defined in advance to optimize for fast SQL queries. Results are normally used for operational analysis and reporting. The overall data is cleaned, transformed, and enriched to act as a single source that users can trust.
Now, a data lake is different in terms of how it stores relational data. This is from a line of business applications that also includes non-relational data from mobile apps, social media, and IoT devices. In data lakes, the schema or data structure is not defined once the data is captured. This implies that you can hold all of your data without detailed design or the need to know what questions you may require answers for in the future. There are different kinds of analytics for your data, such as SQL, big data analytics, real-time analytics, machine learning, and full-text search; these can be used to cover up insights.
As organizations and firms with data warehouses see the benefits of data lakes, they expand their warehouses to include them and allow diverse capabilities, use cases, advanced capabilities, and data science to discover new information models.
What is the Importance of Data Lakes?
Data lakes are important since they act as a central repository for storing and providing all sorts of data, unstructured, raw, and structured, allowing further exploration and detailed analysis. This is a flexible way for businesses to improve decision-making, uncover new insights, and adapt to evolving needs and trends in the market without being restricted by pre-defined data formats.
Here are a few more ways how data lakes empower businesses of all sizes towards improvement:
Improve R&D Innovation
Data lakes can help your R&D teams test and refine assumptions, test their hypotheses, and assess results. This will help you choose the right materials for your product design, resulting in faster performance, comprehending customers’ willingness to pay for different attributes, and conducting genomic research that will lead to more effective medication.
Increased Operational Efficiencies
The Internet of Things (IoT) provides additional methods for collecting and aggregating data on processes. This includes real-time data obtained from internet-connected devices. Storing and analyzing machine-generated IoT data in a data lake makes it easy to identify ways to improve quality and reduce operational costs.
Improved Customer Interactions
A data lake can consolidate customer data from a CRM platform along with social media analytics and a marketing platform that includes purchasing history and incident tickets. This empowers the business to gain insight into the most profitable customer cohort, the reasons for customer churn, and identify promotions or rewards that will increase customer loyalty.
What are the Challenges of Data Lakes?
While the data lake strategy is a reliable method for gaining valuable insights, it can also come with certain challenges that may impede your objectives. Many of these issues arise from a lack of understanding about data lakes and how to extract data from them. Another challenge can stem from improper implementation of the data and the involvement of unskilled personnel. Therefore, it is recommended that professionals be enlisted to assist in managing and presenting your data in an organized and easily accessible manner.
Security is yet another factor that could potentially disrupt your goals, as data lakes often gather unstructured and raw data from various sources. Therefore, it’s important to be cautious about which data you extract and how you intend to use it to achieve your objectives.
Another compelling reason to consider hiring professional data miners and Business Intelligence experts is the maintenance and management of data. For example, if you are handling a large volume of data and find yourself overwhelmed during the maintenance and organization process, it could be a waste of time and money and slow down your overall efficiency if this practice continues.
Gain more knowledge: Everything You Need to Know About Data Mining.
How BiExpertz Can Support Your Data Lakes Requirements
BiExpertz provides the most sustainable, secure, scalable, and cost-effective solutions to all your data requirements. We empower businesses in all sectors to gain the best in the market and achieve their target goals. From organized data visualization, data consolidation, reporting automation, real-time monitoring, business analytics, and self-service BI, our approach enables customers to design their data lakes and further understand and utilize their data for business success.
FAQ’S
What is an example of a data lake?
HTML, JSON, and XML files are some examples of data lakes. These may have hierarchical structures that require further processing to become fully structured.
What is a data lake vs. a data warehouse?
Data warehouses store clean, structured data optimized for specific analyses, while data lakes store any type of data (structured, unstructured) in its raw form, allowing for flexible exploration and future analysis possibilities.
What is the purpose of data lakes?
A data lake is a centralized depository designed to hold, secure, and process large amounts of structured, semi-structured, and unstructured data.
Why were data lakes created?
Data lakes emerged to overcome data warehouse limitations. While warehouses offer strong analytics, they’re costly, inflexible, and struggle with the variety and volume of modern data.