Data Lakes – The key to streamlining the SAS analytics pipeline

Data lakes capture a range of data on a large scale, making it perfectly suited for taking in raw data and quick processing,.

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 traditional analytics pipeline

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.

Streamlining the data analytics pipeline with data lakes

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 does not need to be moved around

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.

Data is easier to shift to new analytical/AI platforms

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.

They improve the quality of analytics models

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.

Data governance is more streamlined

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.


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