Data Lake

What is a Datalake? – Meaning, Benefits, Example & More

In 2021, the world produced, copied, captured, and consumed an unprecedented amount of data, estimated at around 79 zettabytes. This 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 users data falls under the second and third category. Unfortunately, many companies lack the strategies to turn this 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 stats and process it quickly for use whenever it arises? The solution is simple! You can get data lake solutions for your company!

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

What is Data Lake Meaning?

So what exactly is data lake definition?

Experts define a data lake as a highly scalable, centralized repository storage that contains big data in its raw and granular format, gathered from different sources from log files, click-streams, etc. This data can be structured, semi-structured, or unstructured. Unlike most databases, Data Lakes stores data in a flexible form for future use.

Just like a container stores things, it enables 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 details using various tools, such as analytics and machine learning technology with visual dashboards.

Data lakes provide benefits to every industry vertical. You can use a it 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 & object storage, with minor variations in details. The fundamental structure remains the same.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

Difference between Data Lakes and Data Warehouses and a Data Lakehouse

Aspect	Data Lake	Data warehouse	Data Lakehouse
Purpose	Store raw data in its native format	Store processed and structured data	Combines the features of both, providing an integrated platform for both raw and structured data
Data Structures	Schema-on-read	Schema-on-write	Schema-on-read and schema-on-write capabilities
Data Types	Supports structured, semi-structured, unstructured data	Primarily structured data	Supports structured, semi-structured, unstructured data
Data Ingestion	Ingests data from various sources with minimal processing	Ingests data from structured sources after transformation	Ingests data from various sources, to support both raw and processed data
Storage	Typically on inexpensive storage systems from the line of business applications	Typically on expensive storage systems	Typically on cloud data storage systems
Flexibility	Highly flexible, allows for exploratory analysis	Less flexible, optimized for specific queries	Flexible, supports both exploratory and structured analysis
Processing	Requires processing before analysis	Preprocessed for faster querying and analysis	Provides both raw and processed data for analysis
Analytics	Generally lower cost due to storage flexibility	Higher cost due to structured storage and processing	Suited for both big data analytics and traditional business intelligence
Cost	Generally highly durable and lower cost due to storage flexibility	Higher cost due to structured storage and processing	Cost varies depending on the scale and usage
Intended Users	Data Scientists, Data Engineers, Business Analysts, Developers	Business Analysts, Executives and Decision Makers	Unified: Data Engineers, Data Analysts, Business Analysts
Performance	Poor	High	High

Why do you need a data lake?

Are you unsure if your company needs one? 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 collected from various sources like surveillance data, media and entertainment data, invoices, emails, records, and sensor data in the current digital era. Since this type isn’t organized in a specific way, it can be challenging to store and manage effectively. Therefore, it’s critical to implement a sound strategy for storing and processing all the details to 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.

Benefits of Data Lakes?

A data lake can be used to securely store info in a single place for future reference, eliminating problems like data duplication, multiple security policies & collaboration difficulty. 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 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 info. They are less expensive than other tools because they can run on low-cost hardware. With its use, you can be prepared for future increases in volume and have a reliable infrastructure for storing and managing your info.

• 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, which you can import 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 to explore every aspect of information and gain insights over time.

Challenges of Data Lakes

Improper quality assurance, governance & metadata management!

It doesn’t take much time for data lakes to turn into a “data swamp” without proper oversight where finding relevant information and interpreting its quality can become extremely tough. In addition, one can face significant obstacles in ensuring data security & compliance with regulations, particularly in heterogeneous environments with diversity data sources.

Though this platform is flexible, one can face difficulties in maintaining cohesive architectures and in ensuring data access and interpretation consistency.

Data Lake Examples

Below are some of the data lake use cases:

1. Media and entertainment

For aggregation and for utilizing viewer data from streaming platforms & social media customer interactions to get recommendations regarding personalized content and for targeted advertising.

2. Financial Services

For management of portfolio risks, as soon as real-time market data is accessible to the people.

3. Healthcare 

Improvement of care quality of a patient & their pathways using historical data, resulting in improved outcomes and decreasing costs of care.

4. Omnichannel retailers

For capturing and consolidating data that is coming in from various mediums. Examples – mobile apps, word-of-mouth, in-person, etc.

5. Digital supply chain

Helping manufacturing businesses consolidate disparate warehousing data, which can include EDI systems, XML & last but not least JSON.

6. Internet of Things (IoT)

For the future analysis of the enormous amount of data gathered by hardware sensors.

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 this platform, we’ve put together some best practices to ensure it stays organized, efficient, and secure.

Firstly, you need to have clear objectives, management 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 meta data 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 info 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.

Different Developer tools

Here are some open source compute processing tools:

1) Apache Spark

2) Apache Hadoop

3) Presto

Cloud based:

1) Microsoft Azure Data Lake

2) Amazon S3 (storage service)

3) Databricks

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.  

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