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DP-900 · Data Storage Options

23 cards

Data Storage Options

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  1. Match the format to the job

    Before naming a format, name the requirement. A file format should match three things: the type of data being stored, the applications that must read or write it, and whether people need to read the file or systems need efficient storage and processing.

    That last pair is the fork in the road. Human-readable formats are not usually optimized for storage space or processing, and formats optimized for storage and processing are not usually pleasant to open in a text editor. You are choosing which of the two you need, not looking for a format that is best at everything.

    The requirement says Look toward
    People must open and inspect the file Delimited text, JSON, XML
    Many applications must exchange structured rows Delimited text such as CSV
    Hierarchical or varying records JSON
    Storage and processing efficiency at scale Parquet, Avro, Delta Lake
    Images, video, audio, application documents Raw binary
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  2. Quick check

    Which set of considerations should drive the choice of a data file format?

    1. AThe physical medium and the age of the source system

      Neither the medium nor the age of the system is among the stated considerations for choosing a format.

    2. BThe data type, the applications involved, and whether people or systems read the file

      Right. The format should match the type of data, the applications that read or write it, and whether human readability or storage and processing efficiency is required.

    3. CThe number of records, provided the format is human-readable

      Record count alone settles nothing, and human readability is one consideration among several rather than a default.

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  3. Delimited text keeps data readable

    Comma-separated values, or CSV, stores plain-text fields separated by commas and rows terminated by a new line. The first line can optionally contain the field names.

    CustomerID,Name,City
1001,A. Ferrer,Girona
1002,B. Okafor,Lagos

    CSV has close relatives that differ only in what separates the fields.

    Format Separator
    Comma-separated values A comma
    Tab-separated values A tab
    Space-delimited A space
    Fixed-width No separator; each field is allocated a fixed number of characters

    Delimited text is suitable for structured data that must be human-readable and accessible to many applications. That is its whole reason to exist: anyone can open it, and almost anything can parse it.

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  4. Quick check

    A team must exchange structured rows with many applications, and people must be able to inspect the files in a text editor. Which format fits the requirement?

    1. ACSV, because delimited text is broadly accessible and human-readable

      Right. Delimited text is suitable for structured data that must be human-readable and accessible to many applications.

    2. BAvro, because binary records are designed to display as readable rows

      Avro holds data as binary records, so it is not the format to reach for when people must read the file directly.

    3. CA raw binary object, because media formats are the standard for tabular fields

      Raw binary is how images, video, audio, and application documents are stored, not how tabular fields are exchanged.

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  5. JSON and XML carry hierarchy

    JavaScript Object Notation, or JSON, represents objects with attributes and supports hierarchical structure. An attribute can itself contain an object or a collection of objects, which is why JSON handles both structured and semi-structured data.

    { "orderId": 5501,
  "customer": { "id": 1001, "city": "Girona" },
  "lines": [ { "sku": "A-1", "qty": 2 }, { "sku": "B-7", "qty": 1 } ] }

    Extensible Markup Language, or XML, is human-readable and uses tags to define elements and attributes. Tags are written inside angle brackets. XML is more verbose than JSON but remains in use in some systems, so recognizing it matters even when a new design would not choose it.

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  6. JSON and XML carry hierarchy

    Some data is not text at all. Files such as images, video, audio, and application-specific documents are stored as raw binary that an application must interpret and render. Data professionals commonly call these files binary large objects, or BLOBs.

    Keeping the four text-and-binary families apart is most of the recall this topic asks for.

    Format Shape Recognize it by
    CSV Plain text Fields separated by commas, rows ended by a new line
    JSON Plain text Braces, collections, and name-value attributes forming a hierarchy
    XML Plain text Elements and attributes defined by tags in angle brackets
    BLOB Raw binary Media or application documents that an application must render
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  7. Quick check

    Which format represents hierarchical objects and can hold structured or semi-structured data?

    1. AA binary large object rendered by an application

      A binary large object is raw binary that an application renders, such as an image or a video file.

    2. BComma-separated values in plain text

      Delimited text stores flat rows of fields; it has no way to nest an object inside an attribute.

    3. CJavaScript Object Notation

      Right. JSON represents objects with attributes, an attribute can contain an object or a collection of objects, and that hierarchy suits structured and semi-structured data.

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  9. Optimized formats trade readability for efficiency

    When storage space, compression, and processing speed matter more than reading the file by eye, two formats dominate — and they are built on opposite layouts.

    Parquet is a columnar format that stores data for each column together within row groups and uses metadata to locate relevant chunks. Because a query can jump to the chunks holding only the columns it needs, Parquet supports efficient compression and processing, including of nested data.

    Avro is row-based, stores its schema in a JSON header, and holds data as binary records. Keeping whole records together suits moving data around: Avro is useful for compression and for reducing storage and network bandwidth.

    Parquet Avro
    Layout Columnar: columns grouped within row groups Row-based: whole records together
    Schema Metadata locates relevant chunks Stored in a JSON header
    Payload Compressed column chunks Binary records
    Strength Compression and processing, including nested data Compression, reduced storage and network bandwidth
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  10. Quick check

    Which comparison of Parquet and Avro is correct?

    1. AParquet uses XML tags for elements, while Avro uses comma-delimited text rows

      Angle-bracket tags belong to XML and comma-delimited rows to CSV; neither describes these two formats.

    2. BParquet is columnar, while Avro is row-based with a JSON header and binary records

      Right. Parquet stores each column's data together within row groups, and Avro is row-based with its schema in a JSON header and its data as binary records.

    3. CParquet is row-based with a JSON header, while Avro groups columns in row groups

      This reverses the two layouts: the JSON header belongs to Avro and the row groups of column data belong to Parquet.

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  11. Delta Lake adds transactions to Parquet

    Parquet is efficient but, on its own, a set of files. Delta Lake builds on Parquet by adding a transaction log for ACID transactions, data versioning, and reliable updates.

    The log is what changes the behavior. Because every change is recorded before it counts, a reader never sees half of a write, an earlier version of the data can still be identified, and an update that fails does not leave the files in an inconsistent state.

    Layer What it provides
    Parquet Columnar storage with efficient compression and processing
    Delta Lake on top A transaction log giving ACID transactions, data versioning, and reliable updates

    Note what it does not do: it adds no fixed-width text layout, no XML tag hierarchy, and no graph model. It adds transactional guarantees to files that already exist.

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  12. Quick check

    What does Delta Lake add to Parquet files?

    1. AA transaction log supporting ACID transactions, versioning, and reliable updates

      Right. Delta Lake builds on Parquet by adding a transaction log for ACID transactions, data versioning, and reliable updates.

    2. BA fixed-width text layout that gives each field the same number of characters

      Fixed-width is one of the delimited text layouts, and it has nothing to do with Parquet or with transactions.

    3. CAn XML tag hierarchy defining the elements and attributes of every record

      Tags in angle brackets belong to XML; Delta Lake leaves the Parquet payload as it is and adds a log beside it.

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  13. File stores and databases are the two categories

    Above the level of formats sit two broad categories of data store: file stores and databases. Getting the category right comes before choosing anything else.

    Important organizational files are commonly kept in a central shared file storage system rather than only on local disks or removable media. Hosting that central location in the cloud can provide cost-effective, secure, and reliable storage for large volumes of data.

    A database is a dedicated system for storing, managing, and querying data records rather than files. The distinction is not about size or importance — it is about whether the store keeps files that applications open directly, or records that the system itself manages and answers questions about.

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  14. Quick check

    Which rule correctly separates a file store from a database?

    1. AA file store applies table keys, while a database is limited to removable media

      Table keys belong to a relational database, and central file storage exists precisely to replace local disks and removable media.

    2. BA file store keeps files directly, while a database manages and queries data records

      Right. Files are kept in a file store, while a database is a dedicated system for storing, managing, and querying data records rather than files.

    3. CA file store manages relational records, while a database keeps files without querying

      This states the distinction backwards: managing and querying records is the database's role, not the file store's.

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  15. Relational databases connect tables with keys

    Relational databases commonly store structured data in tables. Each entity instance has a primary key, and other tables can use that key to reference the entity.

    That reference is what supports normalization, including avoiding the repeated storage of the same entity details. An order table holds a customer key rather than a duplicate copy of the customer's name, address, and phone number, so a corrected address is corrected once.

    Table Holds Reference
    Customer One row per customer, with CustomerID as its primary key —
    Order One row per order CustomerID pointing at the customer

    Relational tables are managed and queried with Structured Query Language.

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  16. Quick check

    An order system must store structured customers and orders, give each entity a unique identifier, and connect each order to its customer without repeating the customer details. Which design fits?

    1. AA relational database whose tables are connected by primary-key references

      Right. A relational database stores structured entities in tables, gives each instance a primary key, and lets other tables reference it, which is what avoids repeating the same entity details.

    2. BA shared file store holding unrelated binary objects without managed records

      A file store keeps files rather than managed records, so it cannot enforce identifiers or connect entities.

    3. CA graph database that replaces the tables and keys with nodes and links

      A graph model records connections between entities, but the requirement here is exactly the key-linked table design of a relational database.

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  17. Nonrelational models fit other shapes

    Nonrelational databases do not apply a relational schema to their data. They are a family, not one alternative, and each member matches a different record shape.

    Model How a record is stored
    Key-value Each record is a unique key with an associated value
    Document A specialized key-value store whose value is a JSON document
    Column-family Logically related columns grouped into column families
    Graph Entities stored as nodes, with links defining the relationships between them

    Read the table as a set of questions about the data. Is a record just a value fetched by its key? Is it a document with its own fields? Are its columns naturally grouped? Or is what matters most the connections between entities?

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  18. Nonrelational models fit other shapes

    That last question is the graph model's whole purpose. When data is highly connected — and the connections themselves are the thing being queried — a graph database represents them directly rather than reconstructing them from keys at query time.

    The contrast with its neighbors is what makes the choice recognizable:

    • A document database parses each value as a JSON document, but it does not record links between documents as first-class data.
    • A column-family database groups related columns; the grouping says nothing about relationships between entities.
    • A relational database does connect entities, but through key values in tables rather than through nodes and links.
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  19. Quick check

    Which nonrelational model represents highly connected data by recording the connections between entities directly?

    1. AA column-family database with related columns organized into groups

      Column families group logically related columns; the grouping does not express relationships between entities.

    2. BA document database that stores each value as a JSON document

      A document database is a specialized key-value store whose value is a JSON document, not a record of links.

    3. CA graph database with entities as nodes and relationships as links

      Right. A graph database stores entities as nodes and uses links to define the relationships between them.

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  20. Identify the category before the product

    Start with what the workload must store and how applications must access it.

    Choose a file store when applications need to work with files directly — human-readable exports, optimized analytical files, or binary media. Choose a database when the workload needs a dedicated system to manage and query records. Within databases, choose a relational design for structured tables connected by keys, or match a nonrelational model to key-value, JSON document, column-family, or graph-shaped records.

    Work in that order and the answer is usually settled before any product name appears.

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  21. Identify the category before the product

    Two worked cases show how far the requirement alone takes you.

    An Azure solution needs a central cloud location where several applications work directly with important data files. Nothing in that requirement mentions managed records or queries, so the supported decision is the cloud-hosted shared file storage category — files, centrally stored, reachable by the applications that need them.

    A use case centered on managed and queryable records points to the database category instead, and its record shape then chooses between relational and a nonrelational model.

    These criteria identify the kind of Azure datastore needed without assigning a product name that the use case does not establish. Naming a service the requirement never justified is a guess dressed up as an answer.

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  22. Quick check

    An Azure solution needs a central cloud location where several applications work directly with important data files, and the requirement mentions no managed records or queries. Which category should be identified?

    1. AA graph database for entities represented as nodes joined by links

      Nothing in the requirement describes entities and relationships to be queried, and a graph store manages records rather than files.

    2. BA relational database for structured records connected across tables

      A relational design answers a requirement for managed, queryable records connected by keys, which this one does not state.

    3. CA cloud-hosted shared file store for centrally managed files

      Right. When applications must work with centrally stored files and no managed records or queries are required, the supported decision is the cloud-hosted shared file storage category.

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  23. Key takeaways

    • Choose a format from the requirement: the data type, the applications that read or write it, and whether people must read the file or systems need storage and processing efficiency.
    • Know the four families: CSV and its delimited relatives for readable structured rows, JSON for hierarchical objects, XML for tag-defined elements, and raw binary large objects for media and application documents.
    • Optimized formats split by layout: Parquet is columnar with metadata over row groups, Avro is row-based with a JSON header and binary records, and Delta Lake adds a transaction log for ACID transactions, versioning, and reliable updates.
    • Two store categories: file stores keep files; databases manage and query records.
    • Within databases: relational tables link entities with primary keys and are queried with SQL, while key-value, document, column-family, and graph models each match a different record shape.
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  24. Quick check

    Which statement matches the storage options described here?

    1. AParquet stores data column by column, and a graph database records entities as nodes with links

      Right. Parquet is the columnar format storing each column's data within row groups, and a graph database stores entities as nodes with links defining their relationships.

    2. BDelta Lake replaces Parquet with XML, and a document database stores each value as a column family

      Delta Lake builds on Parquet rather than replacing it, and a document database stores its value as a JSON document.

    3. CCSV is a binary format, and a relational database avoids primary keys through normalization

      CSV is plain text, and normalization in a relational database depends on primary keys rather than avoiding them.

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