Data Formats in Big Data Analytics

A data serialization system that is language-neutral, meaning it can be used with different programming languages. Data is described using a schema, which can be written in JSON, and data is encoded in a binary format.

A language-independent description of the structure of data. It acts like a blueprint, defining field names, data types, and other characteristics.

The mechanism that allows Avro to handle changes in the schema over time, ensuring compatibility between different versions of data. This allows for graceful evolution of data without breaking existing applications.

A data format designed for efficient storage and query operations. It leverages columnar storage to enhance performance, especially for analytical workloads.

A columnar data format designed for high-performance analytics used in various data processing frameworks. Its main focus is on efficiency, especially when dealing with large datasets.

The process of converting data from an in-memory format to a format suitable for storage or transmission (like on-disk or over a network).

The process of converting data from a storage format (like on-disk) back into an in-memory representation that applications can work with.

A data format used in Hadoop for storing and retrieving data. It is typically tied to a specific programming language, limiting its interoperability with other systems.

A format for storing data that utilizes a schema for data definition and validation. It supports both generic and specific data serialization, and provides efficient data processing.

A mechanism in Avro where the schema is not explicitly defined during serialization or deserialization. It relies on the schema being available during the process, allowing for dynamic schema evolution.

An approach in Avro where the schema is specified and known beforehand during serialization and deserialization. It requires a pre-generated class based on the schema.

A file format used by Avro for storing sequences of Avro objects. It includes a header with metadata, a sync marker, and blocks of serialized objects. This design enables efficient splitting and processing for MapReduce.

A mechanism in Avro that allows the reader and writer to use different schemas, as long as they are compatible. This enables schema evolution without breaking existing data.

A mechanism in Avro that defines the order in which records are sorted. It allows for efficient data processing and retrieval in sorted sequences.

A group of rows in a Parquet file, containing data for a set of columns.

A section of a Parquet file containing data values for a specific column in a row group.

A smaller unit within a column chunk, containing data for a portion of a column.

The data for each column chunk in a Parquet file is written in pages.

Arrow aims to provide a standardized format for processing data efficiently across different systems (databases, programming languages, libraries) by reducing serialization and deserialization costs.

Arrow leverages columnar storage, where each column is stored contiguously in memory. This improves processing speed and efficiency.

The ability of Arrow to efficiently access any data point in a column at a constant time, regardless of its position.

Arrow makes use of Single Instruction, Multiple Data (SIMD) instructions for parallel processing and leverages vectorization techniques to optimize data manipulation.

A sequence of Unicode characters.

A 32-bit signed integer.

A 64-bit signed integer.

A single-precision (32-bit) IEEE 754 floating-point number.

A double-precision (64-bit) IEEE 754 floating-point number.

A sequence of 8-bit unsigned bytes.

A collection of named fields of any type.

A set of named values, like a list of options.

A fixed number of 8-bit unsigned bytes, representing fixed-length data.

A union of schemas, allowing data to match one of the specified types.

Apache Parquet is a columnar storage format designed for efficient handling of nested data, optimizing file size and query performance.

Columnar formats store data in columns, enhancing efficiency in file size and query performance.

Parquet excels at storing deeply nested data structures, common in real-world applications.

Parquet primarily utilizes primitive data types for data representation.

A schema defines the data structure in a Parquet file, organizing fields with their types, names, and repetition levels.

Groups in Parquet represent nested structures, with plain groups forming nested records.

The UTF8 annotation converts binary data into UTF-8 encoded strings.

The ENUM annotation defines a set of predefined values for a field.

The DECIMAL annotation represents decimal numbers with arbitrary precision, stored in various data types.

The DATE annotation represents a date with no time component, stored as an integer.

The LIST annotation represents ordered collections of data, allowing for repeated data points within a group.

The MAP annotation represents unordered key-value pairs within a group, useful for storing associative data.

A Parquet file consists of a header, blocks, and a footer, containing metadata and data structures.

Each block in a Parquet file stores data in a row group, representing a group of rows.

Adding a new field to an existing schema while maintaining backward compatibility.

A special value assigned to a new field that defaults when the field is not present in older data files.

A combination of data types that allows a field to hold different values, including null.

A reader schema can be used to read only a subset of fields from an Avro data file.

The ability to use different field names when reading data compared to the names used when the data was written.

The "order" attribute can be specified for fields in a record to control the sort order.

Fields are compared pairwise in the order defined by the reader's schema.

A specific schema defines how Avro data is structured and encoded.

Avro provides specialized classes for processing Avro data within MapReduce frameworks.

A data record representing weather information, containing year, temperature, and station ID.