It has become common in the modern business world that big data, which is the large volume of data gathered for analysis by organizations, is a major part of any business strategy. Whether it is operations, sales, marketing, finance, human resources, or any other department, each one of them depends on big data solutions to stay competitive in the market. Although, how organizations handle that big data is vital to the benefits they gain from it. Hence, Data Lake Solutions provides organizations with the tools to improve their Data Security and Management.
The growth in the amount of unstructured data is a challenge to modern organizations. Over the last decade, there has been rapid growth in data creation and inventive transformations in the way information is processed. The increased number of portable devices represents the development of various data formats such as binary data (images, audio/video) CSV, logs, XML, JSON, or unstructured data (emails, documents) that are challenging for database systems.
Maintenance of data flows of all data access points create issues for commonly used data warehouses based on relational database systems. It is often found that with the quick application development, companies may not even have an idea of how the data will be processed, but they have a strict target to use it at several points. While it’s possible to save unstructured data in the RDBMS system, it can be expensive and complex.
Here, you can enter the world of data lakes. Data lakes are storage houses that can comprise data from numerous sources. Other than data processing for direct analysis, all coming data is stored in its relative format. This model enables data lakes to store massive amounts of data while utilizing the least resources. Data is only processed at the time of usage, while in a data warehouse, all incoming data is processed. Ultimately, it enables data lakes to be a proficient method for storage, resource management, and data preparation.
Do you really require a data lake, particularly if your big data solution already comprising a data warehouse? The answer will be a loud ‘yes’. In a world where huge data volumes are shared across limitless devices continues to grow, a resource-competent means of accessing data is vital for success. Here are the following reasons why the requirement for a data lake is getting more urgent with time;
1. 90% of Data Has Been Produced Since 2016
90% of all data is a lot—or is it? Wi-Fi, smartphones and high-speed data networks have become a part of everyday life for the last twenty years. At the starting of the 2000s, streaming was restricted only to audio, while broadband internet was utilized regularly for web surfing, downloading, and emailing. In that condition, device data was at the least amount and the actual data used was generally about interpersonal communication, particularly because videos and TV hadn’t been part of the process, which encouraged high-quality streaming. When the decade came to an end, smartphones had become commonplace and Netflix had transited its business priority to streaming.
It means the internet has experienced huge growth in smartphone applications, social media, streaming services (audio and video), streaming video game platforms, downloaded software rather than physical media between 2010 and 2020, all creating exponential use of data. Is this period of growth significant to business? Imagine how many businesses have connected apps that are continuously transferring data to and from devices, for controlling appliances, deliver instructions and specifications, or gently convey user metrics in the background.
In 2019, deployment of 5G data networks broadly started, so the bandwidths and speeds only got better. Hence, the quantity of data will only get more as technology lets the world get even more connected. Is your data lake ready for it?
2. 95% of Businesses Hold Unstructured Data
In today’s digital world, businesses assemble data from all types of sources, and most of them are unstructured. Think about the data collected by a company that sells services and schedule appointments through an app. While several data streams come in predefined structured formats and fields like phone numbers, dates, time stamps, transaction prices, etc. still, the company has to archive and store a large amount of unstructured data. Unstructured data can be any type of data that doesn’t enclose an inbuilt structure or predefined model, which makes it hard to search, sort, and evaluate without additional preparation.
For example, unstructured data comes in a variety of formats. When a user makes an appointment, all the text fields filled make that appointment sum up to the unstructured data. Emails and documents are other types of unstructured data within a company. The social media posts of the company and photos or videos that are taken by employees as notes during the services are also counted as unstructured data. Similarly, any instructional videos or podcasts created by the company as marketing assets are also unstructured.
3. 50% of Businesses Trust Big Data to Improve their Sales and Marketing
Many people believe big data is beneficial in aspects of its technical usage. Undoubtedly, a company that works via a smartphone app or presents a form of streaming uses big data and is providing a service that just wasn’t possible twenty years ago. However, big data is much more than offering streaming content. It can generate a lot of important improvements in sales and marketing. Based on a report by McKinsey, 50% of businesses believe that big data is empowering them to modify their approach in these departments.
All You Need Is A Data Lake!
The above indicates one point that your organization needs a data lake. And if you don’t prioritize data management, it’s obvious that your competitors will overtake you in areas such as operations, sales, marketing, communications, etc. Data is basically a part of life today, providing precise data-driven decisions and unparalleled insights into deep causes. When collaborated with machine learning and artificial intelligence, you can also use this data for predictive modeling to forecast future events.
How Can You Improve the Security of Data?
Data lakes are a competent and safe way to save all of your incoming data. Worldwide big data is predicted to rise from 2.7 zettabytes to 175 zettabytes by 2025, which means there will be exponential growth, all coming from a growing number of data sources. They are not like data warehouses, where structured and processed data is required. Data lakes work as a single repository for raw data across multiple sources.
Along with a list of benefits, a data lake also has some inbuilt risk of a single point of breakdown. Obviously, it’s uncommon for IT departments to identify an exact single point of failure in today’s IT world. Backups, redundancies, and other typical foolproof techniques are liable to protect company data from correct disastrous failure. It provides double security, so when enterprise data stays in the cloud, data delegated in the cloud rather than the local environment has the additional benefit of trusted vendors creating their own protection systems for your data.
It doesn’t necessarily mean that your data lake is safe from all threats? As with all technologies, a true evaluation of security risks needs a 360-degree view of the situation. Before you step into a data lake, don’t forget to consider these six ways to keep your configuration safe and protect your data.
Establish Governance: A data lake is constructed to store all data. As a storehouse for raw and unstructured data, it can consume anything from any source. But that doesn’t essentially mean that it has to. The sources you choose for your data lake should be scrutinized for how that data will be processed, managed, and used. The threats of a data swamp are very real and keeping them at bay depends on the quality of numerous things like the sources, the data coming from the sources, and the rules for data ingestion. By setting up governance, it’s possible to recognize things such as ownership, security rules for responsive data, data history, source history, and much more.
Access: One of the major security risks concerned with data lakes is associated with data quality. Rather than a macro-scale issue like a whole dataset coming from a single source, risk can come from specific files within the dataset, either when ingesting or after due to hacker access. For example, malware can cover within an apparently gentle raw file, waiting for implementation. Another probable vulnerability arises due to user access if sensitive data is not correctly confined, it’s possible for corrupt users to access those records, perhaps even adjust them.
By building strategic and strict rules for function-based access, it’s possible to reduce the risks to data, especially sensitive data or raw data that has yet to be inspected and processed. Generally, the broadest access should be for data that has been established to be clean, correct, and ready to use, thus restraining the possibility of accessing a potentially harmful file or gaining unsuitable access to susceptible data.
Use Machine Learning: Some data lake platforms come with integral machine learning (ML) functionalities. The usage of ML can considerably reduce security risks by increasing the speed of raw data processing and classification, mainly if used in combination with a data cataloging tool. By this level of automation, a large quantity of data can be processed for common use while also spotting red flags in raw data for added security exploration.
Partitions and Hierarchy: When data is ingested into a data lake, it’s vital to save it in an appropriate place. The common harmony is that data lakes need numerous standard zones to hold data based on how reliable it is and how ready-to-access it is. The various zones are:
- Temporal: Where transient data like copies and streaming reels remains before deletion.
- Raw: Where raw data stays before processing. Data in this zone can also be further encrypted if it encompasses sensitive data.
- Trusted: Where data that has been confirmed as reliable stays for trouble-free access by data analysts, scientists, and other end users.
- Refined: Where enhanced and influenced data stays, generally as final outputs from tools.
You can create a hierarchy by using zones like these, when joined with role-based access, can help lessen the prospect of the wrong people using potentially sensitive or malevolent data.
Data Lifecycle Management: Which data is continuously in use across your organization? Which data hasn’t been touched for years? Data lifecycle management is the process of recognizing and segmenting stale data. In a data lake ecosystem, older stale data can be shifted to a definite tier designed for competent storage, making sure that it is still available whenever needed but not captivating the required resources. A data lake driven by ML can even utilize automation to recognize and process stale data to make the best use of overall efficiency. While this should not impact directly on security issues, a competent and well-supervised data lake enables it to work like a well-oiled machine rather than failing under the burden of its own data.
Data Encryption: The proposal of encryption is very important to data security is not anything new, and most data lake platforms bring their own methods for data encryption. Of course, it is critical to know how your organization implements. In spite of which platform you utilize or what you choose between on-premises vs. cloud, a powerful data encryption strategy that works with your current infrastructure is completely vital to protect all of your data, whether it is in motion or at rest.
Let’s Create Your Data Lake!
What’s the most suitable method to make a secure data lake? By selecting the best range of products, you can create a data lake in just a few steps. With cutting-edge data lake solutions, you get advanced capabilities to integrate it with best-in-class analytics tools. Are you considering creating a data lake? Contact leading service providers to get answers to your major concerns!