SAP C_BW4H_2404 - SAP Certified Associate - Data Engineer - Data Fabric

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Total 80 questions

Which recommendations should you follow to optimize BW query performance? Note: There are 3 correct answers to this question.

Create linked components.

Include fewer drill-down characteristics in the initial view.

Use matory characteristic value variables.

Use the include mode within filter restrictions.

Use the dereference option for reusable filters.

Explanation:

Optimizing BW query performance is critical for ensuring efficient reporting and analysis in SAP BW/4HANA. Letâ€™s analyze each option to determine why B, C, and D are correct:

Explanation: Including too many drill-down characteristics in the initial view of a BW query can significantly impact performance. Each additional characteristic increases the complexity of the query and the volume of data retrieved, leading to slower response times. By limiting the number of characteristics in the initial view, you reduce the amount of data processed upfront, improving query performance.

[: In SAP BW/4HANA, best practices recommend designing queries with a minimal set of characteristics in the initial view and allowing users to add drill-downs dynamically as needed., , 2. Use mandatory characteristic value variables (Option C)Explanation: Mandatory characteristic value variables force users to specify filter values before executing a query. This reduces the amount of data retrieved by the query, as the system only processes the filtered subset of data. Without mandatory variables, queries may retrieve large datasets unnecessarily, leading to poor performance., Reference: SAP BW/4HANA provides variable types such as single-value, multiple-value, and interval variables that can be marked as mandatory. These variables help optimize query execution by narrowing down the data scope., , 3. Use the include mode within filter restrictions (Option D)Explanation: The "include mode" in filter restrictions ensures that only the specified values are included in the query result. This is more efficient than using "exclude mode," which requires the system to evaluate all possible values and exclude the specified ones. Using include mode reduces the computational overhead and improves query performance., Reference: SAP BW/4HANA recommends using include mode for filters wherever possible, as it simplifies the filtering logic and enhances performance., , 4. Create linked components (Option A)Explanation: Linked components are used to create reusable query elements, such as structures or formulas, but they do not directly impact query performance. While linked components improve maintainability and consistency, they are not a performance optimization technique., Reference: Linked components are primarily a design-time feature and do not influence runtime query execution., , 5. Use the dereference option for reusable filters (Option E)Explanation: The dereference option for reusable filters allows filters to be reused across multiple queries. However, this does not inherently improve query performance. The performance impact depends on how the filters are defined and applied, not on the reuse mechanism itself., Reference: Reusable filters are a design-time feature aimed at reducing redundancy, but they do not directly optimize query execution., , ConclusionThe correct answers areB (Include fewer drill-down characteristics in the initial view),C (Use mandatory characteristic value variables), andD (Use the include mode within filter restrictions). These recommendations directly address query performance by reducing data volume and optimizing filtering logic., , ]

Question # 2

Which features of an SAP BW/4HANA InfoObject are intended to reduce physical data storage space? Note: There are 2 correct answers to this question.

Reference characteristic

Transitive attribute

Compounding characteristic

Enhanced master data update

Explanation:

In SAP BW/4HANA, InfoObjects are fundamental building blocks used to define characteristics (attributes) and key figures in data models. They play a critical role in organizing and managing master data and transactional data. Certain features of InfoObjects are specifically designed to optimize storage and reduce physical data redundancy. Below is a detailed explanation of the correct answers:

Explanation: A reference characteristic allows one characteristic to "reuse" the master data and attributes of another characteristic. Instead of duplicating the master data for the referencing characteristic, it simply points to the referenced characteristic's master data. This significantly reduces physical storage space by avoiding redundancy.

[: In SAP BW/4HANA, reference characteristics are commonly used when multiple characteristics share the same set of values (e.g., "Country" as a reference for "Shipping Country" and "Billing Country"). This feature aligns with SAP Data Engineer - Data Fabric principles of optimizing data storage and minimizing duplication., , Option B: Transitive attributeExplanation: A transitive attribute is an attribute that is derived from another characteristic rather than being stored directly in the master data table of the main characteristic. For example, if "City" has an attribute "Region," and "Region" has an attribute "Country," then "Country" can be defined as a transitive attribute of "City." This avoids storing the "Country" attribute redundantly in the "City" master data table, thereby reducing physical storage requirements., Reference: Transitive attributes are a key feature in SAP BW/4HANA for optimizing master data storage. By leveraging relationships between characteristics, they ensure thatonly necessary data is stored, adhering to the principles of efficient data management in SAP Data Engineer - Data Fabric., , Option C: Compounding characteristicExplanation: A compounding characteristic is used to create a hierarchical relationship between two characteristics, where one characteristic depends on another (e.g., "Street" compounded with "City"). While compounding helps organize data logically, it does not inherently reduce physical storage space. Instead, it defines how data is structured and queried., Reference: Compounding is primarily a modeling feature and does not contribute to storage optimization. Therefore, this option is incorrect., , Option D: Enhanced master data updateExplanation: The enhanced master data update mechanism improves the process of updating master data by enabling parallel processing and reducing update times. However, it does not directly reduce physical storage space. Its purpose is to enhance performance and efficiency during data updates, not to optimize storage., Reference: While enhanced master data update is a valuable feature in SAP BW/4HANA, it is unrelated to reducing physical storage space, making this option incorrect., , SummaryTo reduce physical data storage space in SAP BW/4HANA, the following features of InfoObjects are used:, Reference characteristic: Reuses master data from another characteristic, avoiding duplication., Transitive attribute: Derives attributes indirectly through relationships, minimizing redundant storage., These features align with the SAP Data Engineer - Data Fabric's focus on efficient data modeling and storage optimization., , , ]

Question # 3

For which reasons should you run an SAP HANA delta merge? Note: There are 2 correct answers to this question.

To decrease memory consumption

To combine the query cache from different executions

To move the most recent data from disk to memory

To improve the read performance of InfoProviders

Explanation:

In SAP HANA, thedelta mergeoperation is a critical process for managing data storage and optimizing query performance. It is particularly relevant in columnar storage systems like SAP HANA, where data is stored in two parts: themain storage(optimized for read operations) and thedelta storage(optimized for write operations). The delta merge operation moves data from the delta storage to the main storage, ensuring efficient data management and improved query performance.

To Decrease Memory Consumption (A):The delta storage holds recent changes (inserts, updates, deletes) in a row-based format, which is less memory-efficient compared to the columnar format used in the main storage. Over time, as more data accumulates in the delta storage, it can lead to increased memory usage. Running a delta merge moves this data into the main storage, which is compressed and optimized for columnar storage, thereby reducing overall memory consumption.

To Improve the Read Performance of InfoProviders (D):Queries executed on SAP HANA tables or InfoProviders (such as ADSOs, CompositeProviders, or BW queries) benefit significantly from data being stored in the main storage. The main storage is optimized for read operations due to its columnar structure and compression techniques. When data resides in the delta storage, queries must access both the delta and main storage, which can degrade performance. By running a delta merge, all data is consolidated into the main storage, improving read performance for reporting and analytics.

Why Run an SAP HANA Delta Merge?

To Combine the Query Cache from Different Executions (B):This is incorrect because the delta merge operation does not involve the query cache. The query cache in SAP HANA is a separate mechanism that stores results of previously executed queries to speed up subsequent executions. The delta merge focuses solely on moving data between delta and main storage and does not interact with the query cache.

To Move the Most Recent Data from Disk to Memory (C):This is incorrect because SAP HANA's in-memory architecture ensures that all data, including the most recent data, is already stored in memory. The delta merge operation does not move data from disk to memory; instead, it reorganizes data within memory (from delta to main storage). Disk storage in SAP HANA is typically used for persistence and backup purposes, not for active query processing.

Incorrect Options:

SAP Data Engineer - Data Fabric Context:In the context ofSAP Data Engineer - Data Fabric, understanding the delta merge process is essential for optimizing data models and ensuring high-performance analytics. SAP HANA is often used as the underlying database for SAP BW/4HANA and other data fabric solutions. Efficient data management practices, such as scheduling delta merges, contribute to seamless data integration and transformation across the data fabric landscape.

For further details, you can refer to the following resources:

SAP HANA Administration Guide: Explains the delta merge process and its impact on system performance.

SAP BW/4HANA Documentation: Discusses how delta merges affect InfoProvider performance in BW queries.

SAP Learning Hub: Provides training materials on SAP HANA database administration and optimization techniques.

By selectingA (To decrease memory consumption)andD (To improve the read performance of InfoProviders), you ensure that your SAP HANA system operates efficiently, with reduced memory usage and faster query execution.

Question # 4

Which are purposes of the Open Operational Data Store layer in the layered scalable architecture (LSA++) of SAP BW/4HANA? Note: There are 2 correct answers to this question.

Harmonization of data from several source systems

Transformations of data based on business logic

Initial staging of source system data

Real-time reporting on source system data without staging

Explanation:

TheOpen Operational Data Store (ODS)layer in theLayered Scalable Architecture (LSA++)of SAP BW/4HANA plays a critical role in managing and processing data as part of the overall data warehousing architecture. The Open ODS layer is designed to handle operational and near-real-time data requirements while maintaining flexibility and performance. Below is an explanation of the purposes of this layer and why the correct answers areAandC.

A. Harmonization of data from several source systems

The Open ODS layer is often used to harmonize data from multiple source systems. This involves consolidating and standardizing data from different sources into a unified format.

For example, if you have sales data coming from different ERP systems with varying structures or naming conventions, the Open ODS layer can be used to align these differences before the data is further processed or consumed for reporting.

[: In SAP BW/4HANA, the Open ODS layer supports harmonization by providing tools like transformations and mappings to ensure consistency across data from disparate sources. This is particularly important in heterogeneous landscapes where data integration is a challenge., C. Initial staging of source system data, The Open ODS layer serves as an initial staging area for raw data extracted from source systems. It provides a temporary storage point where data can be landed and prepared for further processing or analysis., This staging capability ensures that data is available in its original form (or minimally transformed) for downstream processes, such as loading into other layers of the LSA++ architecture or enabling real-time reporting., Reference: The Open ODS layer is optimized for high-performance data ingestion and staging, making it ideal for scenarios where data needs to be quickly loaded and made available for operational reporting or further transformation., , Incorrect Options:B. Transformations of data based on business logic, While transformations can occur in the Open ODS layer, this is not its primary purpose. The Open ODS layer focuses on initial data staging and harmonization rather than complex business logic transformations., Business logic transformations are typically performed in subsequent layers of the LSA++ architecture, such as theData Propagation Layer (DPL)or theCore Data Warehouse Layer (CDWH)., Reference: SAP recommends keeping the Open ODS layer lightweight and focused on operational and near-real-time use cases, avoiding heavy transformations that could impact performance., D. Real-time reporting on source system data without staging, The Open ODS layer does support real-time reporting, but it requires data to be staged first. The layer acts as an intermediate storage point where data is landed and processed before being made available for reporting., Reporting directly on source system data without staging is typically achieved throughVirtual Data Models (VDMs)orSAP HANA Live, which bypass the need for staging entirely., Reference: The Open ODS layer is not designed for direct, real-time access to source system data without staging. Its role is to stage and prepare data for operational reporting or further processing., , Conclusion:TheOpen ODS layerin SAP BW/4HANA's LSA++ architecture is primarily used forharmonizing data from multiple source systemsand serving as aninitial staging area for source system data. These purposes align with its role in supporting operational and near-real-time reporting while maintaining flexibility and performance. The correct answers are thereforeAandC., , ]

Question # 5

You create an SAP HANA HDI Calculation View.

What are some of the reasons to choose the data category Cube with Star Join instead of data category Dimension? Note: There are 3 correct answers to this question.

You can combine master data transactional data.

You can persist transactional data.

You can provide default time characteristics.

You can create restricted columns.

You can aggregate measures as a sum.

Explanation:

When creating an SAP HANA HDI Calculation View, choosing thedata category Cube with Star JoinoverDimensiondepends on the specific requirements of your data model. Below is a detailed explanation of why the verified answers are correct.

Data Category Dimension:

Used for modeling master data or reference data.

Does not support measures or aggregations.

Typically used for descriptive attributes (e.g., customer names, product descriptions).

Data Category Cube with Star Join:

Used for modeling transactional data with measures and dimensions.

Supports star schema designs, combining fact tables (measures) and dimension tables (attributes).

Enables advanced features like aggregations, time characteristics, and joins between master and transactional data.

Star Join:

A star join connects a fact table (containing measures) with dimension tables (containing attributes) in a star schema.

It is optimized for performance and scalability in analytical queries.

Key Concepts:

Option A: You can combine master data transactional data.

Why Correct?The Cube with Star Join data category is specifically designed to combine transactional data (fact tables) with master data (dimension tables). This enables comprehensive reporting and analysis.

Option B: You can persist transactional data.

Why Incorrect?Persisting transactional data is not a feature of the Cube with Star Join data category. Persistence is typically handled at the database or application layer.

Option C: You can provide default time characteristics.

Why Correct?The Cube with Star Join data category supports default time characteristics (e.g., fiscal year, calendar year), which are essential for time-based reporting and analysis.

Option D: You can create restricted columns.

Why Incorrect?Restricted columns are a feature of calculation views but are not specific to the Cube with Star Join data category. They can also be created in Dimension views.

Option E: You can aggregate measures as a sum.

Why Correct?The Cube with Star Join data category supports aggregations, such as summing measures. This is a key feature for analyzing transactional data.

Verified Answer Explanation:

SAP HANA Modeling Guide:The guide explains the differences between data categories like Dimension and Cube with Star Join, highlighting their respective use cases.

SAP Note 2700850:This note provides examples of scenarios where Cube with Star Join is preferred over Dimension, emphasizing its ability to handle transactional data and aggregations.

SAP Best Practices for HANA Modeling:SAP recommends using Cube with Star Join for analytical models that require combining master and transactional data, providing default time characteristics, and performing aggregations.

Question # 6

You created an Open ODS view of type Facts.

With which object types can you associate a field in the Characteristics folder? Note: There are 2 correct answers to this question.

Open ODS view of type Master Data

InfoObject of type Characteristic

Open ODS view of type Facts

HDI Calculation View of data category Dimension

Explanation:

In SAP Data Engineer - Data Fabric, specifically within the context of Open ODS views, associating fields in the Characteristics folder is a critical task for data modeling. Let's break down the options and understand why A and B are the correct answers:

Explanation: Open ODS views of type "Master Data" are designed to hold descriptive attributes or characteristics that provide context to transactional data (facts). When you create an Open ODS view of type "Facts," you can associate fields in the Characteristics folder with master data objects. This association allows the fact data to be enriched with descriptive attributes from the master data.

[: In SAP BW/4HANA, Open ODS views of type Master Data are often used to model dimensions or attributes that describe the facts. For example, customer or product details can be modeled as master data and linked to fact data., 2. InfoObject of Type Characteristic (Option B)Explanation: An InfoObject of type "Characteristic" is a fundamental object in SAP BW/4HANA that represents a business attribute or property. These InfoObjects can be used to define characteristics in the Characteristics folder of an Open ODS view of type Facts. By associating a field with an InfoObject, you ensure consistency and reusability of metadata across the system., Reference: InfoObjects are part of the SAP BW metadata repository and are widely used in modeling scenarios. They provide a standardized way to define and manage characteristics such as customer, product, or region., 3. Open ODS View of Type Facts (Option C)Explanation: Open ODS views of type "Facts" are designed to store transactional data (measures) rather than descriptive attributes. Fields in the Characteristics folder cannot be associated with another Open ODS view of type Facts because this would create redundancy and violate the separation of concerns between facts and characteristics., Reference: The architecture of Open ODS views enforces a clear distinction between fact data (quantitative measures) and characteristic data (descriptive attributes)., 4. HDI Calculation View of Data Category Dimension (Option D)Explanation: While HDI (HANA Deployment Infrastructure) Calculation Views of data category "Dimension" are used in SAP HANA to model dimensional data, they are not directly compatible with Open ODS views in SAP BW/4HANA. Open ODS views operate within the BW/4HANA framework and rely on BW-specific objects like InfoObjects or other Open ODS views for associations., Reference: HDI Calculation Views are part of the native SAP HANA modeling environment and are not natively integrated with BW/4HANA Open ODS views. Therefore,they cannot be directly associated with fields in the Characteristics folder of an Open ODS view., ConclusionThe correct answers areA (Open ODS view of type Master Data)andB (InfoObject of type Characteristic)because these are the only object types that align with the purpose of the Characteristics folder in an Open ODS view of type Facts. They enable the enrichment of transactional data with descriptive attributes while maintaining the integrity and structure of the data model.]

Question # 7

Why do you set the Read Access Type to "SAP HANA View" in an SAP BW/4HANA InfoObject?

To enable parallel loading of master data texts

To use the InfoObject as an association within an Open ODS view

To generate an SAP HANA calculation view data category Dimension

To report master data attributes which are defined in calculation views

Question # 8

Which SAP solutions can leverage the Write Interface for DataStore objects (advanced) to push data into the inbound table of DataStore objects (advanced)? Note: There are 2 correct answers to this question.

SAP Process Integration

SAP Lscape Transformation Replication Server

SAP Data Services

SAP Datasphere

Explanation:

TheWrite Interface for DataStore objects (advanced)in SAP BW/4HANA enables external systems to push data directly into theinbound tableof a DataStore object (DSO). This interface is particularly useful for integrating data from various SAP solutions and third-party systems. Below is an explanation of the correct answers and why they are valid.

A. SAP Process Integration

SAP Process Integration (PI), now known asSAP Cloud Integration (CI), is a middleware solution that facilitates seamless integration between different systems. It can leverage the Write Interface to push data into the inbound table of a DataStore object (advanced).

SAP PI/CI supports various protocols and formats (e.g., IDoc, SOAP, REST) to transfer data, making it a versatile tool for integrating SAP BW/4HANA with other systems.

[: SAP PI/CI is widely used in enterprise landscapes to connect SAP BW/4HANA with external systems, including pushing data via the Write Interface., D. SAP Datasphere, SAP Datasphere(formerly known as SAP Data Warehouse Cloud) is a cloud-based data management solution that integrates seamlessly with SAP BW/4HANA. It can use the Write Interface to push data into the inbound table of a DataStore object (advanced)., SAP Datasphere is designed for hybrid and cloud-first architectures, enabling organizations to consolidate and harmonize data across on-premise and cloud environments., Reference: SAP Datasphere leverages the Write Interface to enable real-time or near-real-time data integration with SAP BW/4HANA, supporting modern data warehousing requirements., , Incorrect Options:B. SAP Lscape Transformation Replication Server, SAP Landscape Transformation Replication Server (SLT)is primarily used for real-time replication of data from SAP ERP systems to SAP HANA or other target systems. While SLT is a powerful tool for data replication, it does not directly use the Write Interface for DataStore objects (advanced)., Instead, SLT replicates data at the database level, bypassing the need for the Write Interface., Reference: SLT operates independently of the Write Interface and is not listed as a supported solution for pushing data into DSOs., C. SAP Data Services, SAP Data Servicesis an ETL (Extract, Transform, Load) tool used for data integration and transformation. While it can load data into SAP BW/4HANA, it does not use the Write Interface for DataStore objects (advanced)., Instead, SAP Data Services typically loads data into staging areas or directly into target objects using standard ETL processes., Reference: SAP Data Services is not designed to interact with the Write Interface, as it relies on its own mechanisms for data loading., , Conclusion:The correct answers areA. SAP Process IntegrationandD. SAP Datasphere, as these solutions are explicitly designed to leverage the Write Interface for DataStore objects (advanced) in SAP BW/4HANA. They enable seamless integration and data transfer between external systems and SAP BW/4HANA., ]

Question # 9

Which SAP BW/4HANA objects support the feature of generating an external SAP HANA View? Note: There are 2 correct answers to this question.

BW query

Open ODS view

Composite Provider

Semantic group object

Explanation:

In SAP BW/4HANA, certain objects support the generation of external SAP HANA views, enabling seamless integration with SAP HANA's in-memory capabilities and allowing consumption by other tools or applications outside of SAP BW/4HANA. Below is an explanation of the correct answers:

A. BW queryA BW query in SAP BW/4HANA can generate an external SAP HANA view. This feature allows the query to be exposed as a calculation view in SAP HANA, making it accessible for reporting tools like SAP Analytics Cloud (SAC), SAP BusinessObjects, or custom applications. By generating an external HANA view, the BW query leverages SAP HANA's performance optimization while maintaining the analytical capabilities of SAP BW/4HANA.

[: SAP BW/4HANA Query Designer includes functionality to expose queries as external HANA views. This is documented in the SAP BW/4HANA Query Design Guide and is part of the broader integration between SAP BW/4HANA and SAP HANA., , B. Open ODS viewOpen ODS views are designed to provide direct access to data stored in SAP HANA tables or other sources. They inherently support the generation of external SAP HANA views, as they are tightly integrated with SAP HANA's modeling capabilities. Open ODS views act as a bridge between SAP BW/4HANA and SAP HANA, allowing data to be consumed directly by external tools or applications via HANA views., Reference: The Open ODS view functionality is a core feature of SAP BW/4HANA, as described in the SAP BW/4HANA Modeling Guide. It is specifically designed to leverage SAP HANA's native capabilities, including the generation of external views., , Incorrect OptionsC. Composite ProviderComposite Providers in SAP BW/4HANA combine data from multiple sources (e.g., InfoProviders, Open ODS views, or HANA tables) into a unified structure for reporting. However, Composite Providers do not directly support the generation of external SAP HANA views. While they can be used within SAP BW/4HANA for reporting purposes, their architecture does not include the ability to expose themselves as HANA views., Reference: The SAP BW/4HANA Modeling Guide explicitly states that Composite Providers are internal to SAP BW/4HANA and do not generate external HANA views., D. Semantic group objectSemantic group objects are used to organize and manage metadata in SAP BW/4HANA. They do not represent physical data structures or support the generation of external SAP HANA views. Instead, they serve as logical containers for grouping related objects, such as InfoObjects or queries, for easier navigation and maintenance., Reference: The SAP BW/4HANA Administration Guide describes semantic groups as organizational tools rather than data modeling or integration components., , ConclusionThe two SAP BW/4HANA objects that support the feature of generating an external SAP HANA view are:, BW query, Open ODS view, These objects enable seamless integration with SAP HANA's in-memory database and allow external tools to consume data modeled in SAP BW/4HANA. This capability underscores the tight integration between SAP BW/4HANA and SAP HANA, leveraging the strengths of both platforms for advanced analytics and reporting., , ]

Question # 10

What should you consider when you set the High Cardinality flag for a characteristic? Note: There are 2 correct answers to this question.

You cannot use this characteristic as a navigation attribute for another characteristic.

You cannot use navigation attributes for this characteristic.

You cannot load more than 2 billion master data records for this characteristic.

You cannot use this characteristic as an external characteristic in hierarchies.

Explanation:

InSAP BW/4HANA, theHigh Cardinalityflag is used to optimize the handling of characteristics with a very large number of distinct values (e.g., transaction IDs, timestamps). However, enabling this flag imposes certain restrictions on how the characteristic can be used. Below is an explanation of the correct answers and why they are valid.

A. You cannot use this characteristic as a navigation attribute for another characteristic.

When theHigh Cardinalityflag is set, the characteristic cannot serve as anavigation attributefor another characteristic. Navigation attributes are used to provide additional descriptive information for a characteristic, but high-cardinality characteristics are not suitable for this purpose due to their large size and potential performance impact.

[: SAP BW/4HANA enforces this restriction to ensure optimal performance and avoid excessive memory consumption during query execution., B. You cannot use navigation attributes for this characteristic., Similarly, a characteristic with theHigh Cardinalityflag cannot havenavigation attributesassigned to it. Navigation attributes add complexity and increase the volume of data processed during reporting, which is incompatible with the optimization goals of high-cardinality characteristics., Reference: SAP BW/4HANA restricts the use of navigation attributes for high-cardinality characteristics to maintain efficient query performance., , Incorrect Options:C. You cannot load more than 2 billion master data records for this characteristic., This statement is incorrect. TheHigh Cardinalityflag is specifically designed to handle characteristics with very large numbers of distinct values, including scenarios where the number of master data records exceeds 2 billion., Reference: SAP BW/4HANA supports high-cardinality characteristics to manage massive datasets efficiently, leveraging SAP HANA's in-memory capabilities., D. You cannot use this characteristic as an external characteristic in hierarchies., While high-cardinality characteristics are not typically used in hierarchies due to their size and complexity, there is no explicit restriction preventing them from being used as external characteristics in hierarchies., Reference: SAP BW/4HANA allows high-cardinality characteristics to be included in hierarchies, but their usage should be carefully evaluated to avoid performance issues., , Conclusion:The correct answers areA. You cannot use this characteristic as a navigation attribute for another characteristicandB. You cannot use navigation attributes for this characteristic, as these restrictions are imposed to optimize performance and memory usage for high-cardinality characteristics in SAP BW/4HANA., ]

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SAP C_BW4H_2404 - SAP Certified Associate - Data Engineer - Data Fabric

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