Data lakes are the key to streamlining data collection and analysis. However, there is no denying the obvious benefits of these lakes but, like most technologies, there are some disadvantages to using a data lake. It’s important for organisations to be aware of its shortcomings before investing in it. This blog post attempts to address some of the problems that come with data lakes. If not implemented properly, the lake could end up hurting the organisation more than benefiting it.
There are several technical and business challenges of using data lakes.
Data lakes are an open-source of knowledge designed to streamline the analytics pipelines. However, the open nature of the lake makes it difficult to implement security standards. The open nature of the lake and the rate data is inputted, makes it difficult to regulate the data coming in. To eliminate this problem, data lake designers should work with data security teams to set access control measures and secure data without compromising loading processes or governance efforts.
However, it’s not just security that’s causing problems with data lakes. It’s also an issue of quality. Data lakes collect data from different sources and pool it in a single location, but the process makes it difficult to check data quality. It is problematic because it leads to inaccurate results when the data is used for business operations. When the data is inaccurate, the findings will be inaccurate, causing a loss of confidence in the data lake and even in the organisation. To resolve this problem, there needs to be more collaboration between data governance teams and data stewards so that data can be profiled, quality policies implemented and have action taken to improve quality.
Metadata management is one of the most important parts of data management. Without metadata, data stewards (those who are responsible for working with the data) would have little choice but to use non-automated tools like Word and Excel. Moreover, data stewards spend most of the time working with metadata, as opposed to actual data. However, metadata is not implemented on data lakes, which is a problem, in terms of data management. The absence of metadata makes it difficult to perform vital big data management functions like validating it or implementing organisational standards. Since there is no metadata management, it becomes less reliable, hurting its value to the organisation.
Data lakes are incredibly useful, but they are not immune to clashes within the organisation. If the organisation’s structure is plagued with red tape and internal politics, then little value can be derived from the lake. For example, if data analysts cannot access the data without obtaining permission, then it holds up the process and hurts productivity. Different departments might also have rules for the same data set, leading to differences in rules, policies and standards. This situation can be somewhat mitigated by having a robust data governance policy in place to ensure consistent data standards across the whole organisation. While there is no denying the value of data lakes, there need to be better governance standards to improve management and transparency.
Identifying data sources in a data lake is not often done, which is a problem in big data management. Categorising and labelling data sources is crucial because it prevents several problems like duplication of data. Yet, this is not done regularly, which is problematic. At the very least, the source of metadata should be recorded and available to users.
Big data management is made much easier with the use of data lakes. However, there are some challenges when it comes to using the centralised repository. These challenges can hinder the use of the data lake because it becomes harder to discover actionable insights when the data is flawed. If there is a problem with the data, then insights are useless. The main challenge of fixing these problems is implementing multi-disciplinary solutions. Fixing problems with data lakes requires comprehensive technical solutions, adjusting business regulations and transforming work culture. However, organisations need to address these problems. Otherwise, they will fail to draw maximum value from their data lakes.
Data lakes are the key to streamlining the SAS analytics pipeline. The volume of data industries collect has grown exponentially, but along with that growth comes several challenges, in regards to processing in the analytics pipeline. It hinders performance and slows down production cycles, in turn, hindering the rate of innovation. While SAS platforms are more than capable of processing large volumes of data, management of data can always be optimised to improve the analytics process. Processing large volumes of data presents a huge challenge for organisations, especially in an age where data is more valuable than oil. How do analytics experts streamline the analytics pipeline to speed up the rate of innovation? By using data lakes.
The best way to explain how a traditional data analytics pipeline works is by using an analogy of a stream. Raw data comes into the pipeline and is stored in a data warehouse to be cleaned and filtered. Once the data is ready, it will be streamed into the SAS analytics platform when needed through AI and visual pipelines. Furthermore, when it comes to developing new analytics models, data engineers have to build new sandboxes different from the production environment. To build and test analytics model, the sandboxes are built with synthesised data.
There are some disadvantages to the traditional method. The process of cleaning and filtering raw data as and when is needed takes up a lot of time, slowing down the rate of production from the SAS model. Furthermore, the process of developing and testing new SAS analytical models takes up a considerable amount of time, time that could have been spent in more productive areas. Moreover, the current method requires SAS analytics engineers to move data around quite frequently.
For example, when data needs to be processed, it needs to be shifted from the source to the tools, slowing down the analytics process. Even worse, embedding data into the analytics pipeline makes it tough to update the tools. Finally, there is the issue of data governance – data security, resiliency, audit, metadata and lineage are much tougher to carry out because data is stored across different sources, forcing the SAS analytics specialists to divert their efforts, amplifying work across the board.
Data lakes capture a broad range of data types on a large scale, making it perfectly suited for taking in raw data and quick processing.
Data lakes bring several benefits that simplify the processing of data.
Data lakes remove the movement of data from source to SAS analytics platform. Removing the need to transfer data streamlines the analytics pipeline. All data is stored in a common source and can be processed by different tools. A common source for all tools means there no longer needs to be different sources for different tools. All SAS analytics tools can draw their data from a single source, making data movement more efficient than before.
Anyone who works in the world of tech and data knows that analytics platforms are never stagnant. Technology is evolving and analytics platforms should either be updated or changed completely, SAS analytics platforms are no exception. Data lakes make the change easier to accomplish because the data is not stored on the analytics platform. It can streamline the entire pipeline because it is much easier to shift over to the new platform.
Data lakes not only simplify the development of SAS analytics models but can also lead to more accurate models. Under the traditional method, analytics models were only developed using synthetic data. However, synthetic data is not always accurate, which often compromises the quality of the model. Data lakes remove this hurdle by providing secure, read-only access of production data that does not compromise SLAs.
The tasks that fall under data governance become more streamlined and easier to accomplish with data lakes. The entire process becomes much easier to accomplish because data is brought from different sources into a unified source. With data being drawn from a single location, it becomes easier to protect data.
Streamlining the entire process
As SAS data analysts, we must always look for ways to make our jobs more efficient and data lakes are one of the best ways to streamline our work in SAS analytics. By streamlining our analytics pipeline, it allows us to become more productive and spend more time on innovating rather than routine work. Streamlining the analytics pipeline with data lakes also provides tremendous value to our clients because it reduces operational costs while improving productivity.