as documents or as wide columns. An important distinction between the two is how data is retrieved from them.
Fully Indexed, Semi-Structured Data
Let’s consider the simple product catalog example again. There are many ways that shoppers might want to search for information about products. If they are looking for a dishwasher, for example, they might want to search based on size or power consumption. When searching for furniture, style and color are important considerations.
{ {’id’: ’123456’, ’product_type’: ’dishwasher’, ’length’: ’24 in’, ’width’: ’34 in’, ’weight’: ’175 lbs’, ’power’: ’1800 watts’ } {’id’:’987654’, ’product_type’: ’chair’, ’weight’: ’15 kg’, ’style’: ’modern’, ’color’: ’brown’ } }
To search efficiently by attributes, document databases allow for indexes. If you use Cloud Datastore, for example, you could create indexes on each of the attributes as well as a combination of attributes. Indexes should be designed to support the way that data is queried. If you expect users to search for chairs by specifying style and color together, then you should create a style and color index. If you expect customers to search for appliances by their power consumption, then you should create an index on power.
Creating a large number of indexes can significantly increase the amount of storage used. In fact, it is not surprising to have total index storage greater than the amount of storage used to store documents. Also, additional indexes can negatively impact performance for insert, update, and delete operations, because the indexes need to be revised to reflect those operations.
Row Key Access
Wide-column databases usually take a different approach to querying. Rather than using indexes to allow efficient lookup of rows with needed data, wide-column databases organize data so that rows with similar row keys are close together. Queries use a row key, which is analogous to a primary key in relational databases, to retrieve data. This has two implications.
Tables in wide-column databases are designed to respond to particular queries. Although relational databases are designed according to forms of normalization that minimize the risk of data anomalies, wide-column databases are designed for low-latency reads and writes at high volumes. This can lead to duplication of data. Consider IoT sensor data stored in a wide-column database. Table 1.2 shows IoT data organized by sensor ID and timestamp (milliseconds since January 1, 1970 00:00:00 UTC). Future rows would feature the same sensor ID but different corresponding timestamps, and the row key would be determined by both.
Table 1.2 IoT data by sensor ID and timestamp
| Sensor ID | Timestamp | Temperature | Relative humidity | Pressure |
| 789 | 1571760690 | 40 | 35 | 28.2 |
| 790 | 1571760698 | 42.5 | 50 | 29.1 |
| 791 | 1571760676 | 37 | 61 | 28.6 |
Table 1.2 is organized to answer queries that require looking up data by sensor ID and then time. It is not well suited for looking up data by time—for example, all readings over the past hour. Rather than create an index on timestamp, wide-column databases duplicate data in a different row key order. Table 1.3, for example, is designed to answer time range queries. Note that a new table must be created with the desired schema to accomplish this—there is no index that is used to support the query pattern.
Table 1.3 IoT data by timestamp and sensor ID
| Timestamp | Sensor ID | Temperature | Relative humidity | Pressure |
| 1571760676 | 791 | 37 | 61 | 28.6 |
| 1571760690 | 789 | 40 | 35 | 28.2 |
| 1571760698 | 790 | 42.5 | 50 | 29.1 |
Unstructured Data
The distinguishing characteristic of unstructured data is that it does not have a defined schema or data model. Structured data, like relational database tables, has a fixed data model that is defined before data is added to the table. Semi-structured databases include a schema with each row or document in the database. Examples of unstructured data include the following:
Text files of natural language content
Audio files
Video files
Binary large objects (BLOBs)
It should be pointed out that data is considered unstructured if it does not have a schema that influences how the data is stored or accessed. Unstructured data may have an internal structure that is not relevant to the way it is stored. For example, natural language is highly structured according to the syntax rules of languages. Audio and video files may have an internal format that includes metadata as well as content. Here again, there is structure within the file, but that structure is not used by storage systems, and that is the reason why this kind of data is classified as unstructured.
Google’s Storage Decision Tree
Google has developed a decision tree for choosing a storage system that starts with distinguishing structured, semi-structured, and unstructured data. Figure 1.1 is based on the decision tree published at https://cloud.google.com/solutions/data-lifecycle-cloud-platform.
Figure 1.1 Choosing a storage technology in GCP
Schema Design Considerations
Structured and semi-structured data has a schema associated with it. Structured data is usually stored in relational databases whereas semi-structured data is often stored in NoSQL databases. The schema influences how data is stored and accessed,