If a company wants to take advantage of the many different benefits of the increasing networking and digitization of our society, then sooner or later it will come up against Big Data: huge volumes of data that have to be collected, stored and analyzed. The data lake has proven to be one of the most effective storage methods. In this article, we explain what a data lake is and why it is so important.
A data lake is a very large digital data store where a variety of data can be stored in their different original raw formats in a common location. This means that the data does not need to be customized, or the native format of the data changed, in order to store it in a data lake.
James Dixon, chief technology officer of Pentaho, coined the term data lake. In the IT scene, this term then became established because the word lake describes the characteristics of a data lake just as well: It is a "body of data" that has not been manipulated and exists in its original form - the data is not changed or structured for storage, but retains its original form.
The data within a Data Lake can be queried in many different ways. Although the data is then structured to match the query, the native format is retained. For the query in the data lake, it does not matter what the raw format of the data is, the query can always access the entire pool.
One of the biggest obstacles with Big Data is the incredible variety of data collected. Scanners, smart devices, financial transactions, and mail, streaming, and social media sources all store data in different formats. This can be anything from structured databases to video files to plain text formats.
Obviously, this makes collecting, storing, and most importantly, analyzing this data incredibly difficult. This is because on traditional data stores, the various data often had to be adapted or modified in order to be stored in a common location. This means, then, that by manipulating the data, an attempt was made to make the format as uniform as possible.
And even if not: At the latest for a query or evaluation of these data, the data sets must then be adapted, since the many different formats were otherwise not usable. This, however, has a strong negative effect on the value of the collected data. Both the quality and the possible uses are severely limited as a result.
As mentioned earlier, the necessary alignment of data in traditional stores has a massive negative effect on the value of the data. First, no manipulated version of a data set can have the same quality as the original data. Conversely, this means that important information is always lost when data sets are modified.
In addition, the possible uses of the data are severely limited. This is because if the data is manipulated for a particular type of query, then it can no longer be used if a different type of data query or analysis is required later. Thus, data loses value incredibly quickly, which in turn means that it is difficult to profit from the collected data in the long term.
With a data lake, all these problems are avoided. Because, as mentioned earlier, all data is stored in its native formats, no matter how different and diverse these file formats are. So the raw data remains unstructured and unprocessed until it is queried. And even when queried, all information is retained and is neither removed nor filtered prior to storage.
This means that the result of a query is the data with the highest quality. However, since the data is preserved in its native format even when queried, one can query the corresponding information over and over again, and in a variety of ways. The value of the data is thus theoretically preserved forever.
We have already explained why a data lake is so important. However, it is important to note that the fact that the format of the data within a data lake remains untouched does not mean that you do not need to maintain or service a data lake. Otherwise, the data lake quickly becomes a swamp: the data swamp. In fact, this is the technical term for data lakes that are no longer accessible or usable by users.
To avoid a data swamp and the resulting useless and expensive data waste, a data lake must be regularly maintained and well organized. Metadata that is as precise as possible is important in order to recognize the actual value of data and to be able to sort out useless or no longer relevant data in good time. Automatic deletion of data that does not meet the minimum requirements extremely reduces the workload. In addition, security or the updating of security measures must not be neglected.