case class AssertionCall(comment: String, name: String, paramValues: List[String], sql: String) extends Product with Serializable

case class AssertionCalls(assertions: Map[String, String]) extends Product with Serializable

case class AssertionDefinition(fullName: String, name: String, params: List[String], sql: String) extends Product with Serializable

case class AssertionDefinitions(assertions: Map[String, String]) extends Product with Serializable

case class Attribute(name: String, type: String = "string", array: Option[Boolean] = None, required: Boolean = true, privacy: PrivacyLevel = PrivacyLevel.None, comment: Option[String] = None, rename: Option[String] = None, metricType: Option[MetricType] = None, attributes: Option[List[Attribute]] = None, position: Option[Position] = None, default: Option[String] = None, tags: Option[Set[String]] = None, trim: Option[Trim] = None, script: Option[String] = None, foreignKey: Option[String] = None, ignore: Option[Boolean] = None) extends LazyLogging with Product with Serializable

A field in the schema.

A field in the schema. For struct fields, the field "attributes" contains all sub attributes

name: : Attribute name as defined in the source dataset and as received in the file
array: : Is it an array ?
required: : Should this attribute always be present in the source
privacy: : Should this attribute be applied a privacy transformation at ingestion time
comment: : free text for attribute description
rename: : If present, the attribute is renamed with this name
metricType: : If present, what kind of stat should be computed for this field
attributes: : List of sub-attributes (valid for JSON and XML files only)
position: : Valid only when file format is POSITION
default: : Default value for this attribute when it is not present.
tags: : Tags associated with this attribute
trim: : Should we trim the attribute value ?
script: : Scripted field : SQL request on renamed column

case class AutoJobDesc(name: String, tasks: List[AutoTaskDesc], area: Option[StorageArea] = None, format: Option[String], coalesce: Option[Boolean], udf: Option[String] = None, views: Option[Map[String, String]] = None, engine: Option[Engine] = None) extends Product with Serializable

A job is a set of transform tasks executed using the specified engine.

tasks: List of transform tasks to execute
area: Area where the data is located. When using the BigQuery engine, teh area corresponds to the dataset name we will be working on in this job. When using the Spark engine, this is folder where the data should be store. Default value is "business"
format: output file format when using Spark engine. Ingored for BigQuery. Default value is "parquet"
coalesce: When outputting files, should we coalesce it to a single file. Useful when CSV is the output format.
udf: : Register UDFs written in this JVM class when using Spark engine Register UDFs stored at this location when using BigQuery engine
views: : Create temporary views using where the key is the view name and the map the SQL request corresponding to this view using the SQL engine supported syntax.
engine: : SPARK or BQ. Default value is SPARK.

case class AutoTaskDesc(name: Option[String], sql: Option[String], domain: String, dataset: String, write: WriteMode, partition: Option[List[String]] = None, presql: Option[List[String]] = None, postsql: Option[List[String]] = None, area: Option[StorageArea] = None, sink: Option[Sink] = None, rls: Option[List[RowLevelSecurity]] = None, assertions: Option[Map[String, String]] = None) extends Product with Serializable

Task executed in the context of a job.

Task executed in the context of a job. Each task is executed in its own session.

sql: Main SQL request to exexute (do not forget to prefix table names with the database name to avoid conflicts)
domain: Output domain in output Area (Will be the Database name in Hive or Dataset in BigQuery)
dataset: Dataset Name in output Area (Will be the Table name in Hive & BigQuery)
write: Append to or overwrite existing data
partition: List of columns used for partitioning the outtput.
presql: List of SQL requests to executed before the main SQL request is run
postsql: List of SQL requests to executed after the main SQL request is run
area: Target Area where domain / dataset will be stored.
sink: Where to sink the data
rls: Row level security policy to apply too the output data.

final case class BigQuerySink(name: Option[String] = None, location: Option[String] = None, timestamp: Option[String] = None, clustering: Option[Seq[String]] = None, days: Option[Int] = None, requirePartitionFilter: Option[Boolean] = None, options: Option[Map[String, String]] = None) extends Sink with Product with Serializable

When the sink *type* field is set to BQ, the options below should be provided.

location: : Database location (EU, US, ...)

Annotations: @JsonTypeName()

case class CometArrayType(fields: CometStructType) extends CometDataType with Product with Serializable

trait CometDataType extends AnyRef

case class CometSimpleType(simpleType: DataType, attribute: Attribute, tpe: Type) extends CometDataType with Product with Serializable

case class CometStructField(sparkField: StructField, attribute: Attribute, tpe: Type) extends CometDataType with Product with Serializable

case class CometStructType(fields: Array[CometStructField]) extends CometDataType with Product with Serializable

case class Domain(name: String, directory: String, metadata: Option[Metadata] = None, schemas: List[Schema] = Nil, comment: Option[String] = None, extensions: Option[List[String]] = None, ack: Option[String] = None) extends Product with Serializable

Let's say you are willing to import customers and orders from your Sales system.

Let's say you are willing to import customers and orders from your Sales system. Sales is therefore the domain and customer & order are your datasets. In a DBMS, A Domain would be implemented by a DBMS schema and a dataset by a DBMS table. In BigQuery, The domain name would be the Big Query dataset name and the dataset would be implemented by a Big Query table.

name

Domain name. Make sure you use a name that may be used as a folder name on the target storage.

When using HDFS or Cloud Storage, files once ingested are stored in a sub-directory named after the domain name.
When used with BigQuery, files are ingested and sorted in tables under a dataset named after the domain name.

directory

: Folder on the local filesystem where incoming files are stored. Typically, this folder will be scanned periodically to move the dataset to the cluster for ingestion. Files located in this folder are moved to the pending folder for ingestion by the "import" command.

metadata

: Default Schema metadata. This metadata is applied to the schemas defined in this domain. Metadata properties may be redefined at the schema level. See Metadata Entity for more details.

schemas

: List of schemas for each dataset in this domain A domain ususally contains multiple schemas. Each schema defining how the contents of the input file should be parsed. See Schema for more details.

comment

: Domain Description (free text)

extensions

: recognized filename extensions. json, csv, dsv, psv are recognized by default Only files with these extensions will be moved to the pending folder.

ack

: Ack extension used for each file. ".ack" if not specified. Files are moved to the pending folder only once a file with the same name as the source file and with this extension is present. To move a file without requiring an ack file to be present, set explicitly this property to the empty string value "".

sealed case class Engine(value: String) extends Product with Serializable

Annotations: @JsonSerialize() @JsonDeserialize()

class EngineDeserializer extends JsonDeserializer[Engine]

final class EngineSerializer extends JsonSerializer[Engine]

case class Env(env: Map[String, String]) extends Product with Serializable

final case class EsSink(name: Option[String] = None, id: Option[String] = None, timestamp: Option[String] = None, options: Option[Map[String, String]] = None) extends Sink with Product with Serializable

When the sink *type* field is set to ES, the options below should be provided.

When the sink *type* field is set to ES, the options below should be provided. Elasticsearch options are specified in the application.conf file.

Annotations: @JsonTypeName()

sealed case class Format(value: String) extends Product with Serializable

Recognized file type format.

Recognized file type format. This will select the correct parser

value: : SIMPLE_JSON, JSON of DSV Simple Json is made of a single level attributes of simple types (no arrray or map or sub objects)

Annotations: @JsonSerialize() @JsonDeserialize()

class FormatDeserializer extends JsonDeserializer[Format]

final case class FsSink(name: Option[String] = None, options: Option[Map[String, String]] = None, format: Option[String] = None, extension: Option[String] = None) extends Sink with Product with Serializable

Annotations: @JsonTypeName()

sealed case class IndexMapping(value: String) extends Product with Serializable

This attribute property let us know what statistics should be computed for this field when analyze is active.

value: : DISCRETE or CONTINUOUS or TEXT or NONE

Annotations: @JsonSerialize() @JsonDeserialize()

class IndexMappingDeserializer extends JsonDeserializer[IndexMapping]

case class JDBCSource(config: String, schemas: List[Map[String, List[String]]]) extends Product with Serializable

final case class JdbcSink(name: Option[String] = None, connection: String, partitions: Option[Int] = None, batchsize: Option[Int] = None, options: Option[Map[String, String]] = None) extends Sink with Product with Serializable

When the sink *type* field is set to JDBC, the options below should be provided.

Annotations: @JsonTypeName()

case class MergeOptions(key: List[String], delete: Option[String] = None, timestamp: Option[String] = None, queryFilter: Option[String] = None) extends Product with Serializable

How dataset are merged

key: list of attributes to join existing with incoming dataset. Use renamed columns here.
delete: Optional valid sql condition on the incoming dataset. Use renamed column here.
timestamp: Timestamp column used to identify last version, if not specified currently ingested row is considered the last

case class Metadata(mode: Option[Mode] = None, format: Option[Format] = None, encoding: Option[String] = None, multiline: Option[Boolean] = None, array: Option[Boolean] = None, withHeader: Option[Boolean] = None, separator: Option[String] = None, quote: Option[String] = None, escape: Option[String] = None, write: Option[WriteMode] = None, partition: Option[Partition] = None, sink: Option[Sink] = None, ignore: Option[String] = None, clustering: Option[Seq[String]] = None, xml: Option[Map[String, String]] = None) extends Product with Serializable

Specify Schema properties.

Specify Schema properties. These properties may be specified at the schema or domain level Any property not specified at the schema level is taken from the one specified at the domain level or else the default value is returned.

mode

: FILE mode by default. FILE and STREAM are the two accepted values. FILE is currently the only supported mode.

format

: DSV by default. Supported file formats are :

DSV : Delimiter-separated values file. Delimiter value iss specified in the "separator" field.
POSITION : FIXED format file where values are located at an exact position in each line.
SIMPLE_JSON : For optimisation purpose, we differentiate JSON with top level values from JSON with deep level fields. SIMPLE_JSON are JSON files with top level fields only.
JSON : Deep JSON file. Use only when your json documents contain subdocuments, otherwise prefer to use SIMPLE_JSON since it is much faster.
XML : XML files

encoding

: UTF-8 if not specified.

multiline

: are json objects on a single line or multiple line ? Single by default. false means single. false also means faster

array

: Is the json stored as a single object array ? false by default. This means that by default we have on json document per line.

withHeader

: does the dataset has a header ? true bu default

separator

: the values delimiter, ';' by default value may be a multichar string starting from Spark3

quote

: The String quote char, '"' by default

escape

: escaping char '\' by default

write

: Write mode, APPEND by default

partition

: Partition columns, no partitioning by default

sink

: should the dataset be indexed in elasticsearch after ingestion ?

ignore

: Pattern to ignore or UDF to apply to ignore some lines

clustering

: List of attributes to use for clustering

xml

: com.databricks.spark.xml options to use (eq. rowTag)

sealed case class MetricType(value: String) extends Product with Serializable

This attribute property let us know what statistics should be computed for this field when analyze is active.

value: : DISCRETE or CONTINUOUS or TEXT or NONE

Annotations: @JsonSerialize() @JsonDeserialize()

class MetricTypeDeserializer extends JsonDeserializer[MetricType]

sealed case class Mode(value: String) extends Product with Serializable

Big versus Fast data ingestion.

Big versus Fast data ingestion. Are we ingesting a file or a message stream ?

value: : FILE or STREAM

Annotations: @JsonSerialize() @JsonDeserialize()

class ModeDeserializer extends JsonDeserializer[Mode]

final class ModeSerializer extends JsonSerializer[Mode]

final case class NoneSink(name: Option[String] = None, options: Option[Map[String, String]] = None) extends Sink with Product with Serializable

Annotations: @JsonTypeName()

case class Partition(sampling: Option[Double], attributes: Option[List[String]]) extends Product with Serializable

sampling: : 0.0 means no sampling, > 0 && < 1 means sample dataset, >=1 absolute number of partitions.
attributes: : Attributes used to partition de dataset.

Annotations: @JsonDeserialize()

class PartitionDeserializer extends JsonDeserializer[Partition]

case class Position(first: Int, last: Int) extends Product with Serializable

How (the attribute should be transformed at ingestion time ?

first: : First char position
last: : last char position

sealed abstract case class PrimitiveType extends Product with Serializable

Spark supported primitive types.

Spark supported primitive types. These are the only valid raw types. Dataframes columns are converted to these types before the dataset is ingested

Annotations: @JsonSerialize() @JsonDeserialize()

class PrimitiveTypeDeserializer extends JsonDeserializer[PrimitiveType]

sealed case class PrivacyLevel(value: String) extends Product with Serializable

How (the attribute should be transformed at ingestion time ?

value: algorithm to use : NONE, HIDE, MD5, SHA1, SHA256, SHA512, AES

Annotations: @JsonSerialize() @JsonDeserialize()

class PrivacyLevelDeserializer extends JsonDeserializer[PrivacyLevel]

case class RowLevelSecurity(name: String, predicate: String = "TRUE", grants: Set[String]) extends Product with Serializable

User / Group and Service accounts rights on a subset of the table.

name: : This Row Level Security unique name
predicate: : The condition that goes to the WHERE clause and limitt the visible rows.
grants: : user / groups / service accounts to which this security level is applied. ex : user:me@mycompany.com,group:group@mycompany.com,serviceAccount:mysa@google-accounts.com

case class Schema(name: String, pattern: Pattern, attributes: List[Attribute], metadata: Option[Metadata], merge: Option[MergeOptions], comment: Option[String], presql: Option[List[String]], postsql: Option[List[String]] = None, tags: Option[Set[String]] = None, rls: Option[List[RowLevelSecurity]] = None, assertions: Option[Map[String, String]] = None, primaryKey: Option[List[String]] = None) extends Product with Serializable

Dataset Schema

name: : Schema name, must be unique among all the schemas belonging to the same domain. Will become the hive table name On Premise or BigQuery Table name on GCP.
pattern: : filename pattern to which this schema must be applied. This instructs the framework to use this schema to parse any file with a filename that match this pattern.
attributes: : Attributes parsing rules. See :ref:attribute_concept
metadata: : Dataset metadata See :ref:metadata_concept
comment: : free text
presql: : Reserved for future use.
postsql: : We use this attribute to execute sql queries before writing the final dataFrame after ingestion
tags: : Set of string to attach to this Schema
rls: : Experimental. Row level security to this to this schema. See :ref:rowlevelsecurity_concept

sealed abstract class Sink extends AnyRef

Once ingested, files may be sinked to BigQuery, Elasticsearch or any JDBC compliant Database.

Annotations: @JsonTypeInfo() @JsonSubTypes()

sealed case class SinkType(value: String) extends Product with Serializable

Recognized file type format.

Recognized file type format. This will select the correct parser

value: : NONE, FS, JDBC, BQ, ES One of the possible supported sinks

Annotations: @JsonSerialize() @JsonDeserialize()

class SinkTypeDeserializer extends JsonDeserializer[SinkType]

case class SqlTask(presql: Option[List[String]], sql: String, postsql: Option[List[String]]) extends Product with Serializable

sealed case class Stage(value: String) extends Product with Serializable

Big versus Fast data ingestion.

Big versus Fast data ingestion. Are we ingesting a file or a message stream ?

value: : FILE or STREAM

Annotations: @JsonSerialize() @JsonDeserialize()

class StageDeserializer extends JsonDeserializer[Stage]

sealed case class Trim(value: String) extends Product with Serializable

Big versus Fast data ingestion.

Big versus Fast data ingestion. Are we ingesting a file or a message stream ?

value: : FILE or STREAM

Annotations: @JsonSerialize() @JsonDeserialize()

class TrimDeserializer extends JsonDeserializer[Trim]

case class Type(name: String, pattern: String, primitiveType: PrimitiveType = PrimitiveType.string, zone: Option[String] = None, sample: Option[String] = None, comment: Option[String] = None, indexMapping: Option[IndexMapping] = None) extends Product with Serializable

Semantic Type

name: : Type name
pattern: : Pattern use to check that the input data matches the pattern
primitiveType: : Spark Column Type of the attribute

case class Types(types: List[Type]) extends Product with Serializable

List of globally defined types

types: : Type list

sealed case class UserType(value: String) extends Product with Serializable

Recognized file type format.

Recognized file type format. This will select the correct parser

value: : SIMPLE_JSON, JSON of DSV Simple Json is made of a single level attributes of simple types (no arrray or map or sub objects)

Annotations: @JsonSerialize() @JsonDeserialize()

class UserTypeDeserializer extends JsonDeserializer[UserType]

case class Views(views: Map[String, String] = Map.empty) extends Product with Serializable

Thisi tag appears in files and allow import of views and assertions definitions into the current files.

class WriteDeserializer extends JsonDeserializer[WriteMode]

sealed case class WriteMode(value: String) extends Product with Serializable

During ingestion, should the data be appended to the previous ones or should it replace the existing ones ? see Spark SaveMode for more options.

value: : OVERWRITE / APPEND / ERROR_IF_EXISTS / IGNORE.

Annotations: @JsonSerialize() @JsonDeserialize()

Packages

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package model

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Packages

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package model

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