[upbeat music] Bary: In earlier videos, I covered databases and data warehouses as types of data storage solutions. Now let's look at data lakes. This is another type of data management solution that stores structured, semi-structured, and unstructured data. Data lakes are repositories for raw data and tend to serve many purposes. For example, they often hold backup data, which helps businesses build resilience against unexpected harm affecting their data. In other words, businesses are protected against data loss. They also hold data that is historic and not relevant to day-to-day business operations. Let's look at a Google Cloud data lake service. One way to classify an organization's requirements for storage is by how often they need to access the data. Cloud storage is a service that enables you to store and serve Binary Large OBject, or BLOB data. BLOBs are typically images, audio, or other media objects. Cloud Storage provides organizations with different options so they can tailor their object storage based on their access needs. In fact, some of the key benefits of Google Cloud Storage are: you can store unlimited data with no minimum amount required, low latency-- you can retrieve your data as often as you'd like-- and you can access it from anywhere in the world. Suppose, for instance, your organization is storing data that is frequently accessed from around the world. This might be data that serves website content, or mobile applications, or streaming videos. For this type of data, Cloud Storage offers multiregional storage. It's ideal for serving content to users worldwide. We talked in the last video about Spotify. Spotify uses Cloud Storage to serve music to users around the world. Because Cloud Storage stores geographically-dispersed copies of your data, your organization is less likely to lose its data in the case of a disaster. Regional storage is also offered by Cloud Storage. This is ideal when your organization wants to use the data locally. It gives you added throughput in performance by storing your data in the same region as your compute infrastructure. This is a great choice for internal use cases such as data analytics and machine learning jobs. For data that will be accessed less often, Cloud Storage offers Nearline, Coldline, and Archive storage classes. Nearline is best for data you don't expect to access more than once per month, such as multimedia file storage or online backups. Coldline is best for data that you plan to access at most once per 90 days or quarter. Archive is best for data that you plan to access at most once per year, such as archive data or as a backup for disaster recovery. Now let's look at another example of Cloud Storage in use. In the financial industry, voice transcription has always been tricky because it's jargon-heavy, and trading conversations are sensitive in nature. Cloud9 Technologies is a company that provides an innovative voice communication and analytics platform specifically built for the unique compliance and management demands of financial markets. Their platform leverages Google Cloud machine learning services to automate voice-to-text transcription of trading conversations. The platform also uses Cloud Storage to house the enormous quantities of information gathered. The data is encrypted by default, and any sensitive information such as names is automatically redacted in the storage process. All right. So far, we've covered three different types of data management systems: databases, data warehouses, and data lakes. Each delivers value to businesses in different ways, enabling them to leverage data at scale. These systems and tools like Pub/Sub, Dataflow, and BigQuery enable businesses to ingest and analyze data. How is that data then served to the business to generate insights? I'll cover the answer in the next video.
