Understanding data lakes - what is a data lake?

What is a Data Lake?

A data lake is a solution for storing large amounts of data easily, all in one location rather than scattered across an organization. This article is an introduction for business leaders to understand what a data lake is, when one could be used, and why it matters to data-driven businesses.

Managing vast amounts of data can be daunting. Traditional data warehouses, while organized, often can’t keep up with the sheer volume and variety of data businesses generate daily. This is where the concept of a data lake comes into play. 

What is a Data Lake vs Data Warehouse? 

Unlike a data warehouse, which is a structured repository, a data lake allows businesses to store all types of data in a raw, unfiltered format. This approach not only offers unprecedented flexibility and scalability but also paves the way for advanced analytics and insights. 

Grasping the data lake definition and how it can be integrated into your data strategy could be the game-changer your business needs to harness the full potential of its data.

Related Article: Data Lake vs Data Warehouse vs Data Mart

Data lake definition and architecture

Understanding Data Lakes

What is a Data Lake?

At its core, a data lake is designed to store a vast amount of data, whether structured, semi-structured, or unstructured, like texts, videos, audio, and more. Think of every email, every spreadsheet, and every database entry as a unique piece of data that can be stored in a data lake without first transforming it. This contrasts sharply with traditional data warehouses, which require data to be cleaned and structured before it can be stored.

If you are wondering–should I store every email, every spreadsheet–the short answer is no. More on that later in the section “How to build/create a data lake?”.

Here are some examples to help you visualize the concept.

Data Lake Analogies

Example 1

Analogies can help understand data lakes. If you’ve ever been to a donation center like Goodwill or Salvation Army, you’ve seen their loading area. Items of all shapes and sizes–furniture, clothes, toys, tools–unloaded as fast as possible, for future sorting and organizing.

The goal of this part of the store is to make it as easy as possible for donors to unload what they bring. This creates a bit of a dumping ground that is unorganized, but it does accomplish the goals–make it easy for the donors to unload quickly, and provide a large space to gather everything to maximize the donations that can be received.

Example 2

Another data lake example is a piggy bank. Coins of any denomination can be placed just by dropping them in. Later, someone has to sort the coins, put them in rolls, and deposit them in the bank.

As an unsorted mass, the coins are not immediately usable–but without the piggy bank, the coins would be scattered everywhere in the house, some even being lost in couch cushions or under dressers. So, even though the piggy bank is disorganized, at least you haven’t lost anything.

Data Lakes Bring Data Together for Analysis

When done right, data lakes are not just storage solutions. They become a strategic asset, allowing data collection and aggregation easily and quickly. They are a foundational element of a data-driven business strategy, bringing lots of data together for analysis.

Data lakes are scalable, flexible, secure, and accessible:

  • Scalable: data lakes can expand to handle any amount of data you throw at them

  • Flexible: data lakes can handle lots of different kinds of data. Not just data from a database but also spreadsheets, images, and documents.

  • Secure: each data set, although different in size and shape, has access controls and governance mechanisms so that only the right people can access any given data set

  • Accessible: data is easy to extract and analyze from a data lake, making it a best practice for data analysis

What is Data Lake Architecture?

The technology underlying a data lake can be complex, but the high-level architecture is clear. The business need is to store vast amounts of data, of differing sizes and shapes, and make it easy to get the data into the data lake.

Data lake architecture flow chart

Storage

At the heart of data lake architecture is the data lake storage layer. This is a cloud bucket, such as AWS S3, Google GCS, or Azure Blob Storage. This is where the data is physically stored. 

Management

The management layer includes data catalog, governance, and security. The catalog provides a way for users to search, browse, or otherwise understand what data is available and locate the dataset they need. Governance is determining and managing who has rights to access each of the data sets. Security ensures only the right people can access the data. The management layer is important because it provides a way to organize what would otherwise be a confusing mess.

Processing

Next is the processing layer, where data is transformed and analyzed. In some data lakes, this layer is skipped–meaning analysts use whichever tools they prefer. Others have data lake analytics built-in such as tools for analyzing and visualizing data (such as a data dashboard). This is a common scenario not limited to data lakes–built-in tools have better integration, but may not be the best tools for the job.

Data Integration

The data integration layer includes data ingestion and data access. It facilitates data lake integration with various sources and consumers. Data ingestion might include file uploads or an API. Then, accessing data stored in the data lake might include an interface that allows SQL queries (as if the data lake were a database), APIs, or data downloads.

In summary, a data lake is multifaceted, and designed to accommodate all types of data in a scalable, secure, and accessible manner. 

Data lake benefits and use cases

The Business Case for Data Lakes

Why Businesses Need a Data Lake

Just like our piggy bank analogy, businesses often need a centralized place to store data from across the enterprise. This is not a technology decision, it is a data strategy to ensure that everyone knows where to find the data they need and to simplify the management of large amounts of data from many different origination sources. 

Some capabilities that data lakes bring to data management include centralization, accessibility, flexibility, and regulatory compliance.

Benefits of data lakes

Benefits of Data Lakes

Including a data lake in your data strategy brings many benefits, including:

  • Streamlined operations and reduced costs – data lakes minimize the need for data preprocessing, allowing raw data storage at scale. This speeds up data ingestion and reduces costs associated with maintaining data transformations

    Related Article: How Much Do Data Analytics Services Cost?
  • Agility – the ability to adapt quickly when new opportunities arise that can be powered by data. Having a data lake already in place allows organizations to leverage their data immediately rather than setting up a new process for each new dataset.
  • Speed – Data lakes provide a consistent, easy way to get data out of operational systems and into a centralized location. Custom procedures no longer need to be created or maintained. 
  • Security and governance: Despite their open nature, data lakes include robust security and governance. The key to that is that centralization makes it easier to monitor and protect sensitive information. 
  • Innovation is unlocked by allowing more people in the company access to data for analysis. This supports a data-driven culture and encourages people to use data in their decision-making.
  • Data-driven cultures become much more achievable when data is at the ready, available for whoever needs it, when they need it.

Data Lake Use Cases

Data lakes have a variety of use cases, but they all come down to a few decision points: do you need the scalability, flexibility, and accessibility of a data lake? At Datateer, we recommend using a data lake sometime after the initial phase of getting data analytics going in a company, rather than a first step. 

Here are some examples of use cases where a data lake makes sense:

  1. Advanced Analytics: Data lakes offer a flexible solution for enterprise data lake management, offering a treasure trove of information. Analysts can sift through this data to identify consumer behavior trends or operational efficiencies. Retailers, for example, can use these insights to tailor product offerings or optimize store layouts to enhance sales. Another example would be how Credit Unions use modern data analytics to analyze member data to gain insights into their financial habits, which allows them to provide tailored recommendations to help customers achieve their financial goals.
  2. IoT Integration: As more devices become internet-enabled, they generate vast amounts of data. Data lakes are capable of storing and analyzing this IoT data, leading to improved efficiencies and insights. For example, manufacturers can use data lakes to predict when machinery will require maintenance, preventing unexpected downtime.
  3. Enhancing Data Warehousing: While data lakes are not a replacement for data warehouses, they complement them by acting as a large-scale data repository where raw data can be initially stored and later processed. This approach provides businesses with a broader dataset for analytics and decision-making.
  4. Personalization and Customer Insights (Customer 360): By integrating various data sources, data lakes help create comprehensive customer profiles. This unified view enables businesses to analyze the customers and offer personalized experiences and services, leading to higher customer satisfaction and loyalty.
  5. Facilitating Research and Development: Data lakes streamline the research process by providing a centralized repository of diverse data types. This can accelerate the pace of research and development, leading to faster analysis and innovation.
Building and choosing tech for data lakes

Implementing and Leveraging Data Lakes

How to build/create a data lake?

Building a data lake requires several steps, each of which is important to a successful outcome. They entail understanding data lake strategy and architecture and selecting the appropriate data lake tools. 

  1. First, define your business objectives. You are about to invest time and resources into a technology–be sure it aligns with your goals. Review carefully the section “The Business Case for Data Lakes” and ensure the outcomes you need can be achieved through a data lake solution. 
  2. Then choose the right data lake platform and technology. Cloud providers AWS, Azure, and Google Cloud offer robust cloud data lake solutions. Likely you are already leveraging other services from one of these providers, which can help make this choice easier. 
  3. Establish governance and data management processes. This can get complicated quickly. I follow a couple of simple rules when designing governance processes: 1) someone must be the business owner of each data asset and 2) no one wants an extra burdensome process. However you design your data governance, keep it clear and simple. 
  4. Define the data ingestion mechanisms–what are the supported ways for data to enter the data lake? For example, at Datateer we allow uploads of files into an AWS S3 data lake or Google GCS buckets. Then we convert and compress the data so that everything is in the same format.
  5. Define how to organize and catalog each dataset. Once data is ingested, it should be findable by organizing and cataloging it. This could mean tagging it with metadata, creating a searchable index, or implementing a full data cataloging tool. Organizing and cataloging ensures data is findable when needed for analysis
  6. Decide supported analysis and processing methods. How will you allow people or systems to access and use the data? Must they export it? Will you have an API or SQL interface? These are technical decisions, but the focus should be on how to maximize accessibility and use of the data from the data lake.
  7. Finally, educate and empower people in the company by involving them along the way. Going back to the first step of defining your business objectives, manage this change in your organization, starting with the people who will benefit most from the new data lake.

Choosing a Data Lake Technology

When you’re ready to adopt a data lake for your business, choosing the right technology is crucial. This decision will impact how effectively your organization can store, manage, and analyze data. 

Assess your needs and be able to articulate them clearly

Anyone involved in this process, whether that’s a data lake vendor/company or your internal team, will benefit from an understanding of the outcomes you are after and the objectives you need to accomplish. 

Consider Compatibility With Your Current Technology Stack

If you are already heavily invested in a cloud provider, the benefits of using that provider’s data lake products outweigh any minor benefits from too much exploration. 

Keep It Simple

Data lakes can be a deep topic, but most of the literature is for advanced use cases for large enterprises with complex needs. Focus on the key components from the sections “What is Data Lake Architecture?” and “Why Businesses Need a Data Lake

Evaluate Scalability

In today’s cloud-driven world, this usually means getting an understanding of how your costs will scale as your data volume and velocity increase.

Data Lake Challenges

Data lakes require a lot of technology to implement and have some serious challenges. The list below contains problems you will likely encounter and some suggestions to overcome them.

  • Data Swamps. By far, the biggest criticism of data lakes is that they become “data swamps”–horribly complicated and unnavigable, data goes into them and is lost forever. This is prevalent if the management layer is weak. The only way to avoid data swamps is to be organized: every dataset must have a business owner, make them findable and understandable, and measure usage to retire or archive datasets that are of no value.
  • Unanticipated storage costs are a common challenge with data lakes. Because the underlying cloud technologies charge on a consumption basis–based on how much data you store and analyze–data lake services are consumption-based. Consumption pricing is hard enough to predict in any case. For data lakes in particular, with their exploratory nature, costs are especially hard to project. Ensure you get a solid understanding of the pricing levers so that you can manage those, rather than just predicting a cost upfront and hoping for the best.
  • Data edge cases are impossible to predict. The flexibility of data lakes is a two-edged sword–flexibility is needed, but it engenders edge cases not originally anticipated. To avoid this, you should develop ground rules about what data formats are accepted, the types of data accepted, volumes, and business needs. Even though data sets in general can be very flexible, your data lake should have ground rules that match your business needs.  
  • Query performance is a problem for analyzing data in a data lake. Because of the flexibility around ingesting data, data lakes are not optimized for querying data. Ensure you have a plan on how to query data when the data lake’s built-in processing is too slow or cumbersome.
  • Processing costs. Beware of data lake vendors that promise easy processing or analysis on top of their data lakes, and spend time in demos going deep on what types of processing is supported, how it performs, and how it affects cost. For example, most vendors will charge on the amount of data scanned when analyzing. If you have a lot of analysis efforts, you may want to add on a policy of when to use the data lake processing (and incur the costs) and when to extract or export the data into other analysis tools, thus avoiding the processing cost.
  • Data quality and consistency. This is one of the most important reasons that companies start with data warehouses instead of data lakes. Because of their flexible nature, data lakes do not focus on data quality and consistency. The data will be full of duplicates, gaps, and inconsistencies–in effect, “dirty data” that needs to be cleansed and prepared before it is useful. Any analysts hoping to use the data will have to spend significant time preparing and cleansing it for you–and they will never do it consistently. One analysis will be cleansed slightly differently–or very differently–leading to different results. 
  • Skill requirements. Data lakes often require advanced data literacy and skills–SQL, Python, R, and advanced spreadsheet usage. This is another reason most organizations start with a data warehouse instead–the audience of potential users is much larger, and business impact can happen more quickly.

Why use a data lake? 

Embarking on the journey to implement a data lake is a strategic decision that requires careful planning and consideration. While data lakes offer numerous benefits including scalability, flexibility, and enhanced analytics capabilities, they also present unique challenges such as the potential for becoming data swamps, unforeseen costs, and the need for robust data governance.

A data lake is not a one-size-fits-all solution. It’s essential to evaluate whether its capabilities align with your data strategy and if your organization is prepared to manage the complexity it introduces. However, with careful planning and the right approach, a data lake can be a valuable asset to your business, unlocking new insights from your data and driving innovation.

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