The DataShapa guide to Data Lakes

18 August 2021

The DataShapa guide to Data Lakes

Recognised for their ability to enhance and optimise data strategies and Business Intelligence processes, Data Lakes are becoming increasingly common within the landscape of enterprises of any size.

However, in a world of warehouses, lakes, pools, ponds and swaps, it is easy for the advantages and essentials of Data Lakes to get lost in a sea of jargon and other similar features. Below, we examine the wealth of advantages that Data Lakes can bring to an organisation, the core differences between a Data Lake and a Data Warehouse, as well as what the future of Data Lakes may resemble.

What are Data Lakes?

Offering a wide range of benefits and use cases, a Data Lake provides access to a repository of both structured and unstructured data, without being constrained by size or capacity.

Differing from Data Warehouses in ways we’ll consider shortly, Data Lakes are able to aid analysts and teams in a variety of use cases, due to holding raw data that can be extracted, repurposed, and recontextualised as needed.

While the modern disruptive view may conclude that, as Data Lakes can contain both structured and unstructured raw data in a scalable solution, there’s no need for alternative data storage solutions. However, this isn’t true. Operating a Data Lake still requires ongoing maintenance and management to ensure consistency in quality and trust in results.

In creating a repository of raw data available to end-users, a Data Lake offers a versatile selection of data that can be modelled in many ways. However, this can cause one of the core difficulties of Data Lakes – inconsistency in results gained. Data pulled out is implemented in a variety of processes and models, meaning that different results can be gained from the same datasets.

This difficulty should be communicated amongst internal teams, as well as demonstrating best practices for interacting with, and generating results from, Data Lakes.

Data Lakes vs Data Warehouses

As businesses of all sizes are beginning to realise the potential of utilising BI processes to achieve data-driven insights, terms such as Data Warehouses and Data Lakes can often be miscommunicated and substituted for one another. With both serving different purposes and needing different maintenance and management requirements, businesses must understand the core differences between these two structures.

While a Data Lake contains a pool of raw data, which has not yet had any purpose assigned and which may be modelled and manipulated in a variety of ways, a Data Warehouse is a structured repository with pre-defined uses. Data has been structured, managed, and stored in a Data Warehouse to be utilised in a specific manner further on.

Currently, Data Warehouses require more initial effort, management, and resources – requiring data to be moderated, structured, and defined before migration.

On the other hand, Data Lakes require more effort when migrating raw data for use in other processes, due to datasets not yet being structured or defined for use.

One of the core benefits of Data Lakes is that it allows a wide variety of data to be utilised and experimented with. However, due to this open, experimental approach to data, the efficacy of a Data Lake will depend on how it is interacted with and utilised, rather than how it serves to store purely structured data – as is the case with a warehouse.

While the quality of a data lake is measured and affected by how data is pulled and used, there are some core advantages to Data Lakes that remain present regardless of the situation.

To learn more about Data Warehouses, read our blog on Data Management principles.

The three core advantages of Data Lakes

Many businesses wishing to enhance their current data strategy and gain access to insights using the full breadth of their collected data are realising the benefits of Data Lakes. Some of these benefits include:

   1. Efficiency

Effective Data Lakes eliminates the need to prepare and model data before ingestion, allowing internal teams additional time to devote to other areas. This allows for the easy inclusion of data without the need to devote large amounts of time and resources to ensuring consistency and quality.

   2. Scalability

Data Lakes are greatly scalable, and at a relatively inexpensive price when compared to that of a Data Warehouse. This need for a scalable approach to data ingestion is more necessary than ever before, with data becoming more available and accessible in vast amounts than previously thought possible. A scalable approach allows teams to freely ingest data as much as they wish, without having to navigate the difficulties of ingest caps.

   3. Versatility

Due to the data held in Data Lakes being raw and unstructured, the use cases of datasets are defined by how they are pulled and modelled. This means that the same dataset can be modelled and utilised in many ways, providing a versatile response to experimental analysis and other BI tools.

The future of Data Lakes

Data Lakes are allowing many enterprises to realise the full potential of their complete datasets, allowing previously unused data to be incorporated into BI processes. Providing a flexible, efficient, and versatile approach to BI, Data Lakes are an important tool that businesses should consider.

However, this doesn’t mean that Data Warehouses will soon be disappearing in favour of Data Lakes.

Ideally, the two platforms should be incorporated alongside one another, with Data Lakes becoming a repository for analytical workloads while Data Warehouses allow for more sophisticated, streamlined, and potentially automated processes that require constant consistency.

As we look to the future of Data Lakes, we expect to see more of an emphasis on efficiency and scalability, while more businesses adopt Data Lakes – which must become more accessible and easily used to cater to a wider audience – after realising the advantages they bring to any BI landscape.

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