- Companies rely upon data lakes to store massive amounts of unstructured data. However, data lakes are not equipped with the necessary tools for deep analysis.
- With Google’s BigLake, users can utilize analysis tools to query their data from a single platform, avoiding the risk and expense of moving data to a platform with more functions.
The ever-increasing amount of data collected by businesses, governments, and related organizations creates both opportunities and problems; the ability to collect and analyze data allows companies to understand their customer’s preferences quickly and accurately. At the same time, the amount of data ingested continues to grow exponentially, overwhelming efforts to manage and analyze the data effectively. Creating even more challenges in data collection has been the shift from the orderly, defined tables of structured data stored in a data warehouse to the exabytes of raw unstructured data, including text messages, audio files, videos, and all of the by-products of the digital world.
Although data lakes have proved useful in storing massive amounts of unstructured data, they have fallen short in helping users explore and analyze the mountains of unstructured data. Attempting to gain control and insight over an expensive but increasingly useless asset, some companies built on-premises data lakes utilizing Apache’s Hadoop framework. However, these efforts largely failed, leaving businesses with impenetrable amounts of data in what some derisively characterized as ‘data swamps.’
Google’s BigLake offers massive amounts of data storage and provides customers with tools to manage and analyze their data on a single platform. To analyze the data from their lakes, companies have been required to transfer the data to different platforms, resulting in delays, information silos, and increased storage expenses. With BigLake, users can manage and query their data from one platform, eliminating the risks of moving large amounts of data to other platforms.
To give BigLake its differentiated capabilities, Google makes use of its BigQuery data warehouse platform and extends it to data lakes stored in Google’s Cloud Storage service. This allows customers to manage and analyze data regardless of the underlying file type. Both BigLake and Google Cloud storage will allow administrators to fine-tune their security through the use of policy tags. These tags will also monitor and ensure that only the targeted data flows to the specified tool such as Spark, Presto, Trino, and TensorFlow. Also, BigLake can be used on other clouds, including Azure Data Lake Storage and Amazon’s S3.
While Google’s data lake offers some interesting functions, it is hardly the first to provide a unified service. Companies such as Snowflake and SAP offer a similar service, leaving Google the latest to join a group of fiercely competitive companies. Google has been racing to catch companies such as Amazon and Microsoft, which rate first and second in market share. BigLake will help Google increase its share, but ultimately its success will depend upon the speed and quality of its tools and the value it brings to customers.