A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day. Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day. Which solution will meet these requirements in the MOST operationally efficient way?
Answer(s): A
Here's a detailed justification for why option A is the most operationally efficient solution:Option A leverages AWS Database Migration Service (DMS) for both the initial full load and subsequent incremental updates using Change Data Capture (CDC). DMS is designed for database migrations and replication, making it well-suited for this task. The full load efficiently copies the existing data to S3. The CDC functionality captures changes happening in the Oracle database (between 1 AM and 2 AM) and applies them to the S3 data lake. DMS CDC streams the data without requiring full table scans, enhancing efficiency. The monotonically increasing column provides a natural key for identifying changes.The custom logic in AWS Glue is used to append the incremental data to the full-load copy in S3. Glue provides a serverless environment for data integration and transformation, making it ideal for appending data and managing the data lake. This approach avoids repeated full loads.Option B, while using Glue job bookmarks, would require significant custom coding to handle the CDC-like functionality and append data, making it less operationally efficient than using DMS CDC. Glue is better suited for transformations after CDC.Options C and D both involve performing a full load every day. This is highly inefficient and resource-intensive, especially given the 50 GB table size. Overwriting the entire dataset daily consumes excessive bandwidth, storage, and processing power. The business intelligence team needs to query data between 10 AM and 8 PM daily. Using solutions C and D will make the data unavailable between 1 AM and 2 AM.Therefore, the combination of DMS for full load and CDC, with Glue for final appending, offers the best balance of efficiency, automation, and scalability for the given requirements. It avoids repeated full loads and leverages specialized AWS services for their intended purposes.Supporting Links:AWS Database Migration Service (DMS): https://aws.amazon.com/dms/ AWS Glue: https://aws.amazon.com/glue/ AWS DMS Change Data Capture (CDC): https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Introduction.html
A company is building a data lake for a new analytics team. The company is using Amazon S3 for storage and Amazon Athena for query analysis. All data that is in Amazon S3 is in Apache Parquet format. The company is running a new Oracle database as a source system in the company’s data center. The company has 70 tables in the Oracle database. All the tables have primary keys. Data can occasionally change in the source system. The company wants to ingest the tables every day into the data lake. Which solution will meet this requirement with the LEAST effort?
Answer(s): B
The correct answer is B because it offers the least amount of effort while fulfilling all requirements. Here's a breakdown:AWS Glue: Glue is a fully managed ETL (Extract, Transform, Load) service designed for discovering, preparing, and integrating data. This makes it ideal for ingesting data from the Oracle database into the S3 data lake. AWS Glue Connection: Glue Connections allow Glue jobs to connect to various data sources. This enables a seamless connection to the on-premises Oracle database. AWS Glue Bookmark Job: Glue bookmark jobs provide incremental data ingestion capabilities. Since the data in the Oracle database changes, bookmarking allows Glue to only ingest new or updated records each day, improving efficiency. Parquet Format: Glue supports writing data in Parquet format directly to S3. Least Effort: Setting up a Glue connection and a bookmark job is significantly less complex and time-consuming than setting up EMR with Sqoop, configuring DMS, or migrating the entire database to RDS and setting up triggers and Lambda functions.Why other options are not the best choice:A (EMR with Sqoop): While Sqoop can read from Oracle and write to S3, managing an EMR cluster adds operational overhead compared to the fully managed Glue service. Sqoop also requires more manual configuration and management than Glue's built-in incremental capabilities. C (AWS DMS): DMS is primarily designed for database migration and continuous replication. While DMS can write to S3, it's more geared towards replicating entire tables and might not be the most efficient solution for daily incremental updates and transformations for data lake purposes. D (RDS, DMS, Lambda): Migrating the Oracle database to RDS and implementing triggers and Lambda functions is by far the most complex and time-consuming option. It involves significant database administration and development effort. This is a costly and complex approach for simply ingesting data into a data lake.Supporting Links:AWS Glue Documentation : For a deep understanding of AWS Glue features and functionalities. AWS Glue Connections : For understanding connections to different types of data sources AWS Glue Bookmarks : For detail information on incremental data loading using job bookmarks.
A transportation company wants to track vehicle movements by capturing geolocation records. The records are 10 bytes in size. The company receives up to 10.000 records every second. Data transmission delays of a few minutes are acceptable because of unreliable network conditions. The transportation company wants to use Amazon Kinesis Data Streams to ingest the geolocation data. The company needs a reliable mechanism to send data to Kinesis Data Streams. The company needs to maximize the throughput efficiency of the Kinesis shards.Which solution will meet these requirements in the MOST operationally efficient way?
The correct answer is B: Kinesis Producer Library (KPL). Here's why:The primary goal is to efficiently ingest a high volume (10,000 records/second) of small records (10 bytes each) into Kinesis Data Streams, while maximizing shard throughput and tolerating minor transmission delays. KPL excels in this scenario.KPL Aggregation and Batching: KPL aggregates multiple small records into a single Kinesis Data Streams record, significantly reducing the number of requests and thus increasing throughput. This is crucial because Kinesis Data Streams charges based on PUT requests. Increased Throughput Efficiency: By reducing the number of PUT requests, KPL effectively reduces the number of Kinesis Data Streams records, allowing you to utilize the provisioned throughput of your shards more efficiently. Handles Retries: KPL automatically handles retries for failed requests, increasing reliability which is important given the unreliable network conditions. This reduces operational overhead related to error handling. Asynchronous API: The KPL is designed to be an asynchronous API, increasing the ingestion performance. Reduced Costs: By using aggregation, KPL reduces the amount of Kinesis Data Streams records, thus reducing cost of operating the data streams. Kinesis Agent is less efficient: Kinesis Agent can ingest data but does not inherently provide aggregation/batching functionalities to the same extent as KPL. It's a more basic tool. Kinesis Data Firehose is unsuitable: Firehose delivers data to destinations like S3 or Redshift, and it doesn't provide the same degree of control over shard utilization as KPL offers. Firehose's buffering is more aimed towards long term storage. Kinesis SDK is too low level: Using the Kinesis SDK directly requires implementing aggregation, batching, and retry mechanisms yourself, which adds operational complexity.In summary, KPL's built-in aggregation, batching, and retry capabilities make it the most operationally efficient solution for ingesting a high volume of small records into Kinesis Data Streams while maximizing shard throughput and tolerating short data transmission delays.Authoritative Links:Kinesis Producer Library (See "Producers" Section) Developing Producers Using the Amazon Kinesis Producer Library
An investment company needs to manage and extract insights from a volume of semi-structured data that grows continuously. A data engineer needs to deduplicate the semi-structured data, remove records that are duplicates, and remove common misspellings of duplicates. Which solution will meet these requirements with the LEAST operational overhead?
The most efficient solution for deduplicating semi-structured data with misspellings, while minimizing operational overhead, is using AWS Glue's FindMatches feature. FindMatches is specifically designed for record linkage and deduplication tasks. It uses machine learning algorithms to identify near-duplicate records, even with variations in spelling or formatting, in a scalable and automated manner. Options B, C, and D are less suitable. Athena, while useful for querying data, requires manual SQL queries for deduplication, increasing operational overhead. While it can be done using techniques like ROW_NUMBER() and PARTITIONBY , it does not inherently handle fuzzy matching or misspellings effectively. This increases the complexity of the query and requires more hands-on work to configure rules for detecting duplicates based on text similarity or string distance. Neptune ML, a graph database service, would be overkill for this scenario. Creating a graph database and running Gremlin scripts adds significant complexity and overhead compared to a dedicated deduplication tool. DynamoDB's global tables are primarily for multi-region replication and do not inherently prevent or handle duplicate data based on content matching. They only ensure data consistency across regions. The advantage of FindMatches lies in its ability to learn from data samples to create a transformation that identifies similar records using techniques like tokenization, stemming, and fuzzy matching. Its iterative learning process allows it to accurately detect duplicates even when data is inconsistently formatted or contains misspellings, reducing the need for manual configuration and ongoing maintenance. Therefore, FindMatches is the most appropriate choice as it is purpose-built for this scenario, reducing complexity and operational burden.Relevant Links:AWS Glue FindMatches: https://docs.aws.amazon.com/glue/latest/dg/deduplication-findmatches.html Amazon Athena Windows Functions: https://docs.aws.amazon.com/athena/latest/ug/window-functions.html (For alternative solutions, but not preferred here) Amazon Neptune ML: https://aws.amazon.com/neptune/machine-learning/ Amazon DynamoDB Global Tables: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/globaltables.html
A company is building an inventory management system and an inventory reordering system to automatically reorder products. Both systems use Amazon Kinesis Data Streams. The inventory management system uses the Amazon Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Amazon Kinesis Client Library (KCL) to consume data from the stream. The company configures the stream to scale up and down as needed. Before the company deploys the systems to production, the company discovers that the inventory reordering system received duplicated data. Which factors could have caused the reordering system to receive duplicated data? (Choose two.)
Answer(s): A,C
Here's a detailed justification for why options A and C are correct regarding duplicated data in a Kinesis Data Streams consumer (inventory reordering system using KCL) when the producer (inventory management system using KPL) sends data to it:A: The producer experienced network-related timeouts.The KPL guarantees at-least-once delivery. When the producer experiences a network timeout while attempting to send a record to Kinesis Data Streams, it doesn't know if the record was successfully ingested. Therefore, the KPL retries sending the record. If the original record did make it to Kinesis before the timeout, the retry results in a duplicate record being written to the stream. The KCL consumer then reads both the original and the retry, causing duplication. Relevance: Network instability and the producer's retry mechanism introduce the possibility of duplicates, even with at-least-once guarantees. Supporting Concept: Idempotency is a common concept in distributed systems that solves the problem of retrying a service operation (in this case, sending data to kinesis).C: There was a change in the number of shards, record processors, or both.The KCL assigns shards to record processors (consumer instances). When the number of shards in a stream changes (e.g., due to scaling up or splitting shards), or the number of record processors changes (e.g., adding or removing consumer instances), the KCL needs to rebalance the shard assignment. During this rebalancing, it's possible for a record processor to temporarily process records from a shard that was previously being processed by another processor. This can lead to a record being processed twice. Also, if there is a failure of a record processor, the KCL will assign the shards to another record processor. Relevance: The KCL handles shard assignment to ensure data from a shard is read by only one processor at a given time. However, transitions and failures can briefly violate this, causing overlap and duplication. Supporting Concept: Shard assignment and the KCL worker model are central to Kinesis Data Streams consumer processing and must be accounted for when designing idempotent consumer applications.Why the other options are not the primary causes of duplication:B: The stream’s value for the IteratorAgeMilliseconds metric was too high. A high IteratorAgeMilliseconds value indicates that the consumer is lagging behind in processing the data in the stream. While this is an issue (data might expire), it doesn't directly cause duplication. It only means that the consumer is reading data that is old. D: The AggregationEnabled configuration property was set to true. Aggregation in KPL improves throughput by bundling multiple records into a single Kinesis record. While improper deaggregation on the consumer side could potentially lead to data issues, it wouldn't inherently cause duplication of whole Kinesis records. E: The max_records configuration property was set to a number that was too high. The max_records setting in KCL controls the maximum number of records a record processor can retrieve in a single getRecords call.This setting can impact throughput and processing time, but it doesn't directly cause the consumer to process the same records multiple times.Authoritative Links for Further Research:Kinesis Data Streams Developer Guide: Developing Consumers with the KCL 2: https://docs.aws.amazon.com/streams/latest/dev/developing-consumers-with-kcl2.html Kinesis Data Streams Developer Guide: Developing Producers with the KPL:https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html AWS Blog: Building Scalable Stream Processing Applications Using Amazon Kinesis Client Library: https://aws.amazon.com/blogs/big-data/building-scalable-stream-processing-applications-using-amazon-kinesis-client-library/ AWS Kinesis Data Streams FAQs: https://aws.amazon.com/kinesis/data-streams/faqs/ (specifically regarding data durability and delivery guarantees)
An ecommerce company operates a complex order fulfilment process that spans several operational systems hosted in AWS. Each of the operational systems has a Java Database Connectivity (JDBC)-compliant relational database where the latest processing state is captured. The company needs to give an operations team the ability to track orders on an hourly basis across the entire fulfillment process. Which solution will meet these requirements with the LEAST development overhead?
The best solution is A because it offers the least development overhead for tracking orders on an hourly basis across multiple JDBC-compliant databases. AWS Glue can readily connect to JDBC data sources, extract, transform, and load (ETL) data into Amazon Redshift, a data warehouse optimized for analytical workloads. Redshift's columnar storage and query optimization are ideal for aggregating and analyzing order fulfillment data. Amazon QuickSight then allows for the quick creation of dashboards to visualize and track order status.Option B is less suitable as DynamoDB, a NoSQL database, while efficient for high-velocity writes, is not as optimized for complex analytical queries needed to track orders across various stages. This would necessitate additional data transformation and potentially slower query performance.Options C and D utilize AWS DMS for change data capture (CDC). While CDC is useful for near real-time data replication, it introduces complexity. Setting up DMS tasks and ensuring data consistency across different AWS regions increases the development overhead compared to Glue's batch-oriented ETL. Also, replicating to DynamoDB has the same disadvantages as explained in Option B. Furthermore, while both QuickSight and Grafana are viable for dashboarding, QuickSight integrates more seamlessly within the AWS ecosystem, potentially reducing initial setup time and maintenance overhead, making option D less appealing than A. Given the hourly tracking requirement, a simpler, batch-oriented ETL process with Glue to Redshift is sufficient and less complex than a near real-time CDC approach.Therefore, the combination of AWS Glue, Amazon Redshift, and Amazon QuickSight delivers the desired functionality with minimal development effort.Relevant links:AWS Glue: https://aws.amazon.com/glue/ Amazon Redshift: https://aws.amazon.com/redshift/ Amazon QuickSight: https://aws.amazon.com/quicksight/ AWS Database Migration Service (DMS): https://aws.amazon.com/dms/
A data engineer needs to use Amazon Neptune to develop graph applications. Which programming languages should the engineer use to develop the graph applications? (Choose two.)
Answer(s): A,D
The correct answer is A and D: Gremlin and SPARQL. Amazon Neptune is a graph database service and supports graph query languages.Gremlin: This is a graph traversal language widely used for querying and manipulating graph data. Neptune is optimized for Gremlin traversals. It allows developers to express complex graph traversals using a functional programming style. The Apache TinkerPop framework provides Gremlin support across many graph databases.SPARQL: (SPARQL Protocol and RDF Query Language) is a query language specifically designed for Resource Description Framework (RDF) data, which represents data as a graph. Neptune supports SPARQL 1.1, enabling queries against RDF-based graphs.SQL, ANSI SQL, and Spark SQL are relational database query languages, and while they can interact with graph data through various connectors or transformations, they are not native graph query languages supported by Neptune. Neptune is specifically designed to work with graph-structured data and is optimized for graph traversal operations using languages like Gremlin and SPARQL. Using SQL with Neptune would involve complex transformations and would not leverage Neptune's native graph querying capabilities. Therefore, using native graph query languages Gremlin and SPARQL for developing graph applications using Amazon Neptune is the appropriate solution.Relevant links:Amazon Neptune Documentation Gremlin query language SPARQL
A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size. The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer. Which solution will meet these requirements?
The correct answer is A because it effectively addresses the requirements of optimal throughput from devices, dedicated throughput for consumers, and stream processing.Here's a detailed justification:Optimal Throughput: Kinesis Data Streams using the PutRecords API operation is designed for high-throughput data ingestion. PutRecords allows sending multiple data records in a single request, reducing overhead compared to sending records individually. This directly addresses the requirement for optimal throughput from each gaming app device. Dedicated Throughput for Consumers: The enhanced fan-out feature in Kinesis Data Streams enables each consumer to have its own dedicated throughput, independent of other consumers. This feature is critical as the question explicitly requires dedicated throughput for each of the three internal consumers. Without enhanced fan-out, consumers share the stream's read capacity, which can lead to throttling and performance degradation, which goes against the requirements. Stream Processing Application: Kinesis Data Streams is ideal for real-time stream processing. The stream-processing application can read data from the Kinesis stream and perform transformations, analytics, or other operations as needed.Option B is incorrect because Kinesis Data Firehose is primarily designed for loading data into data lakes, warehouses, or analytics services, not for real-time stream processing with dedicated throughput. Firehose does not inherently offer a mechanism for dedicated throughput to different consuming applications. Requesting dedicated throughput from AWS Support is not the correct solution for achieving dedicated throughput for each consumer.Option C is incorrect because while using the Kinesis Producer Library (KPL) with Firehose does improve the throughput, Firehose does not directly support enhanced fan-out which is needed for providing dedicated throughput to internal consumers.Option D is incorrect because while it uses Kinesis Data Streams, simply hosting the stream-processing application on EC2 instances with autoscaling doesn't guarantee dedicated throughput per consumer. Multiple consumers would still be competing for the stream's read capacity.Supporting Links:Amazon Kinesis Data Streams Enhanced Fan-Out: https://aws.amazon.com/kinesis/data-streams/enhanced-fan-out/ Amazon Kinesis Data Streams PutRecords:https://docs.aws.amazon.com/kinesis/latest/APIReference/API_PutRecords.html Choosing Between Kinesis Data Streams and Kinesis Data Firehose: https://aws.amazon.com/blogs/big-data/choosing-between-amazon-kinesis-data-streams-and-amazon-kinesis-data-firehose/
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