What is a data lake?

Although the data lake and the data warehouse use the same design patterns, they have opposite characteristics. Data warehouses structure and package data for quality, consistency, reuse, and high performance. Data lakes, on the other hand, complement data warehouses with a design model that focuses on original raw data accuracy and long-term storage at low cost while providing a new form of analytical agility.

Why Data Lakes Are Important

Data lakes meet the need to economically leverage and generate value from ever-increasing volumes of data. This “dark data” from new sources such as the web, mobile phones, connected devices has often been ignored in the past, but this data contains valuable insights. Large volumes of data and new forms of analysis have led to the need to explore new ways to manage data and derive value from it.

The data lake is where long-term data containers gather that capture, clean, and explore all kinds of raw data at an appropriate scale. Data subsets (data mart) are powered by low-cost technologies that can benefit from many downstream possibilities, including data warehouses, and recommendation engines.

Prior to the big data trend, data integration in a sort of continuum — like a database — normalized information and created that value. This is no longer enough to manage all the data in the business alone, and trying to configure it completely undermines the value. Dark data is therefore rarely captured in a database, but data scientists often search dark data to find a few facts worth repeating.

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The Data Lake and New Forms of Analysis

Technologies such as Spark and other innovations allow programming languages to be parallelized, and this has led to the emergence of a completely new type of analysis. These new forms of analytics, such as graphs, text, and machine learning algorithms that receive a response, then compare that response to the next piece of data, and continue that way until a final output is reached, can be efficiently processed at an appropriate scale.

Data Lake and Enterprise Memory Protection

Archiving data that has not been used for a long time can save storage space in the data warehouse. Until the data lake design pattern emerged, there was no space other than the high-performance data warehouse or offline tape backup unit to put cold data that was occasionally wanted to be accessed. With virtual query tools, users can easily access cold data along with warm and hot data in the data warehouse with a single query.

Data Lake and Data Integration

The industry has gone back and forth on how to best reduce data transformation costs and come to the same place. Data Lake offers more scalability than traditional ETL (extract, convert, upload) servers at low cost, forcing companies to rethink data integration architectures. Businesses using the best modern practices are rebalancing hundreds of data integration jobs across data lake, data warehouse and ETL servers because each has its own capacities and economies.

Challenges in Data Lake Projects

In appearance, data lakes may seem simple, as they offer a way to manage and use structured and unstructured data in huge volumes. However, they are not as simple as they seem, and failed data lake projects are common in many industries and organizations. The first data lake projects faced challenges because best practices had not yet emerged. Now, the main reason why data lakes are not able to give their exact values is the lack of a solid design.

Data silo and cluster dissemination: There is an opinion that data lakes have a low barrier to entry and workarounds can be found in the cloud. This leads to unnecessary data and inconsistency due to the inconsistency of the two data lakes, as well as synchronization problems.

Contradictory goals for data access: There is a balancing act between determining how strict security measures should be and agile access. It is necessary to have plans and procedures that align all stakeholders.

Vehicles ready for limited commercial use: Many providers suggest that they connect to Hadoop or cloud object storage, but the proposals made lack deep integration, and a large number of these products are made for data warehouses for data lakes.

Lack of final user acceptance: Users — rightly or wrongly — have the perception that getting answers from data lakes is too complicated or that they can't find what they're looking for in the data stacks because they require high-level coding skills.

Data Lake Design Pattern

The data lake design pattern provides a set of workloads and expectations that guide successful implementation. As data lake technology and experience have evolved, an architecture and the requirements associated with it have evolved so much that leading providers now have agreements and best practices for applications. Technologies are important, but the design pattern, which is independent of technology, is the most important. A data lake can be built on multiple technologies. Hadoop Distributed File System (HDFS) is what many people first think, but it is not necessary.