Data Lake

Exploring the Data Lake: Everything You Need to Know

Guide to Data Lake

In 2021, the world produced, copied, captured, and consumed an unprecedented amount of data, estimated at around 79 zettabytes. This data volume is expected to continue growing without any signs of slowing down, with projections showing that it will exceed 180 zettabytes by 2025.

It is common for organizations to have various types of data stored in their devices, including structured, semi-structured, and unstructured data. Surprisingly, about 90 percent of this data falls under the semi-structured or unstructured category. Unfortunately, many companies lack the strategies to turn this data into useful information, both from internal and external sources. This makes it difficult for them to have a clear view of their business processes and customers’ behavior patterns, leading to delayed and inadequate decisions, which can result in increased risks. You don’t want to fall into this category, do you? So, how can you store this data and process it quickly for use whenever it arises? The solution is simple! You can get data lake solution for your company!

But what is a data lake? And how does it work? Keep on reading to find out!

What is a Data Lake?

A data lake is a centralized storage repository that contains big data in its raw and granular format, gathered from different sources. This data can be structured, semi-structured, or unstructured. Data lakes store all the information in a flexible form for future use.

Just like a container stores things, data lakes enable you to store both external and internal information, including data from IoT devices, on-premises applications, social media platforms, website clickstreams, and others. You can access and analyze this data using various tools, such as machine learning technology.

Data lakes provide benefits to every industry vertical. You can use a data lake to enhance efficiency and incorporate predictive maintenance. It can help you comprehend the areas and causes of failure that require attention. You can adjust maintenance schedules to reduce repair costs and analyze production efficiency.

Understanding Data Lake Architecture

Data lakes follow a typical architecture process, with minor variations in details. The fundamental structure remains the same.

  • Data Ingestion

This component, as the name suggests, connects data lakes to external non-relational and relational sources such as wearable devices or social media platforms, and it can handle a variety of polished, semi-polished, and unpolished data. The first step, ingestion, can be done either in real-time or in batches. However, when dealing with different types of data, you might need to use multiple technologies to ingest them.

  • Data Landing

After data is received, it is stored in a landing zone. Each piece of data is assigned a unique identifier and metadata tags. The landing zone is usually the largest area used for analysis and operations. Data analysts and scientists work with this raw source data to define its scope and purpose in data lakes.

  • Data Processing

The data is processed only after its purpose is identified. Refinement, aggregation, optimization, and quality standardization take place using various methods. This step is beneficial for several business use cases and reporting needs.

  • Refined Data Zone

After processing, data scientists and analysts develop specific data science strategies to control processing. They repurpose raw information into high-quality structures to assist in analysis or engineering.

  • Consumption Zone

The final phase of the data flow process is the consumption or curated zone. Data scientists use analytic consumption tools and SQL and NoSQL query capabilities to provide the intended audience, such as a business analyst or a technical decision-maker, with the results and insights from the analytic projects.

Why do organizations need a Data Lake?

Are you unsure if your company needs a data lake? Here are some reasons why incorporating one can advance your business:

  • Increase in data generation

The growth in data has been staggering. In the early 2000s, streaming was limited to audio and broadband was mainly used for web surfing, downloading, and emailing, resulting in minimal data usage. However, with over one-third of the population now owning mobile phones and actively engaging in social media, data has become a necessity. As data is constantly being created, it is important to ensure that your repository is capable of storing it all.

  • Amount of unstructured data

As a CISO, CIO, or CTO, you are responsible for managing data in your organization, which includes unstructured data. In today’s digital era, unstructured data can come from various sources, including surveillance data, media and entertainment data, invoices, emails, records, and sensor data. Since unstructured data is not organized in a specific way, it can be challenging to store and manage effectively. Therefore, it’s crucial to adopt a sound strategy for storing and processing all the unstructured data. By doing so, you can ensure that your organization’s data is secure, accessible, and actionable.

  • Consumption of data

The internet is one of the most unique inventions of all time. The amount of data consumed on online platforms worldwide is simply staggering. Google alone handles over 40,000 searches per second, while around 1.5 billion people use social media each day. Data is everywhere, and its global consumption is growing exponentially. Gathering this kind of data is essential for companies to cover all aspects of their operations, from marketing and sales to communication.

  • Deal with the changes big data brings

Many businesses that operate through web or phone applications rely on big data to improve their sales and marketing strategies. Big data refers to large and complex data sets that traditional software finds difficult to process. By utilizing big data, businesses can attract and retain loyal customers. However, to fully benefit from it, you need to establish a proper infrastructure that can receive, retain, and retrieve information from these data sets in a timely manner.

What are the benefits of a Data Lake to an organization?

A data lake can be used to securely store data for future reference. If data is not managed effectively, a business can fall behind in various aspects. Data lakes have no limitations or restrictions on volume, making it easy to access data for training or threat-hunting purposes. Your organization can benefit from data lake solutions in several ways.

  • Greater agility

Change is inevitable, and the business environment is no exception to this rule. Your company will always face new challenges and opportunities that you must be prepared to tackle. Using data lakes instead of traditional tools for data analysis provides you with greater flexibility and adaptability in responding to market or economic changes quickly.

  • Scalability at a reasonable price

Data lakes are a cost-effective option for storing and managing large amounts of data. They are less expensive than other tools because they can run on low-cost hardware. With data lakes, you can be prepared for future increases in data volume and have a reliable infrastructure for storing and managing your data.

  • Instant implementation

You don’t need to follow a lengthy schema-definition process to create a data lake for your organization. The platform can handle unrefined or semi-structured data without requiring any transformation. You can import the data as it is.

  • More data sources

You can store any type of data in its raw form in a data lake. Professionals can use this data to explore every aspect of information and gain insights over time.

Data Lake Best Practices

Data lakes are a powerful way to store and analyze large amounts of diverse data, but they can quickly become a mess without proper planning and implementation. To help you make the most out of your data lake, we’ve put together some best practices to ensure it stays organized, efficient, and secure.

Firstly, you need to define clear objectives and governance policies. This will help you ensure that your data quality, security, and access control are all top-notch. You should also segment your data lake into distinct zones and prioritize data quality and standardization.

Next, invest in data cataloging and metadata management to help you keep track of your data assets. It’s also important to leverage security best practices like robust access controls and encryption to keep your data safe.

When it comes to architecture, you should choose a scalable and cost-effective platform that can accommodate growing data volumes and processing demands. You can also foster a data-driven culture within your organization by promoting data literacy and usage.

Lastly, continuously monitor and improve your data lake architecture and processes based on evolving needs and best practices. Remember, the data lake is an ongoing journey, and continuous improvement is key to unlocking its full potential.

By following these best practices, you can build a data lake that serves as a reliable and valuable foundation for data-driven decision-making within your organization.

How can NewEvol help your company? 

It is important to acknowledge that data has become an integral part of our lives. It enables us to make unmatched discoveries and informed decisions based on precise insights. Incorporating machine learning and artificial intelligence only enhances its significance in corporations. If you’re willing to embrace this, it points towards exceptional data lake solutions.  

The data lake has emerged as a vital tool for corporations, thanks to its practical analytics. With NewEvol Data Lake solution, you can store massive amounts of raw data in its original form and gain insights from petabytes of information in real-time. Our robust solution ensures that you can easily collect and process data, making your business more efficient and effective while keeping your data secure. 

Our NewEvol offers exclusive features you can take advantage of: 

  • It provides a feasible way to analyze data, free from any concerns regarding its size or scale.
  • The platform comes with pre-packaged and effective data ingestion strategies to enable analysis from a central location.
  • It utilizes on-premise security to store data and servers in a data center and assists in managing multi-domain services with its multi-tenancy.

Besides the features, our product promises to benefit you in multiple ways. For instance, 

  • NewEvol’s data lake includes data visualization tools that use graphical elements to analyze vast amounts of data in innovative ways.
  • It provides privacy and data compliance with a mix of standard security specifications. 
  • Its cluster-based structure makes it relatively easier to ingest more data by adding multiple nodes.
Krunal Medapara

Krunal Mendapara is the Chief Technology Officer, responsible for creating product roadmaps from conception to launch, driving the product vision, defining go-to-market strategy, and leading design discussions.

October 4, 2022

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