AWS Glue

Travel operations (SELECT, CREATE, CLONE, UNDROP, etc.) to be performed on it. All Snowflake accounts have a default retention period of 1 day (24 hours). By default, the data retention period for standard objectives is 1 day, while for enterprise editions and higher accounts, it is 0 to 90 days. 6. Explain what is fail-safe. Snowflake offers a default 7-day period during which historical data can be retrieved as a fail-safe feature. Following the expiration of the Time Travel data retention period, the fail-safe default period begins. Data recovery through fail-safe is performed under best-effort conditions, and only after all other recovery options have been exhausted. Snowflake may use it to recover data that has been lost or damaged due to extreme operational failures. It may take several hours to several days for Fail-safe to complete data recovery. 7. Can you explain how Snowflake differs from AWS (Amazon Web Service)? Cloud-based data warehouse platforms like Snowflake and Amazon Redshift provide excellent performance, scalability, and business intelligence tools. In terms of core functionality, both platforms provide similar capabilities, such as relational management, security, scalability, cost efficiency, etc. There are, however, several differences between them, such as pricing, user experience and deployment options. There is no maintenance required with Snowflake as it is a complete SaaS (Software as a Service) offering. In contrast, AWS Redshift clusters require manual maintenance.The Snowflake security model uses always-on encryption to enforce strict security checks, while Redshift uses a flexible, customizable approach.Storage and computation in Snowflake are completely independent, meaning the storage costs are approximately the same as those in S3. In contrast, AWS bypasses this problem with a Red Shift spectrum and lets you query data that is directly available in S3. Despite this, it is not flawless like Snowflake. 8. Could AWS glue connect to Snowflake? Yes, you can connect the Snowflake to AWS glue. AWS glue fits seamlessly into Snowflake as a data warehouse service and presents a comprehensive managed environment. Combining these two solutions makes data ingestion and transformation easier and more flexible. 9. Explain how data compression works in Snowflake and write its advantages. An important aspect of data compression is the encoding, restructuring, or other modifications necessary to minimize its size. As soon as we input data into Snowflake, it is systematically compacted (compressed). Compressing and storing the data in Snowflake is achieved through modern data compression algorithms. What makes snowflake so great is that it charges customers by the size of their data after compression, not by the exact data. Snowflake Compression has the following advantages: Compression lowers storage costs compared with original cloud storage.On-disk caches do not incur storage costs.In general, data sharing and cloning involve no storage expenses. 10. Explain Snowflake caching and write its type. Consider an example where a query takes 15 minutes to run or execute. Now, if you were to repeat the same query with the same frequently used data, later on, you would be doing the same work and wasting resources.Alternatively, Snowflake caches

Instances. Although the Copy command is for fast loading it will work at its best when all the slices of nodes equally participate in the copy command Download the Guide to Select the Right Data Warehouse Learn the key factors you should consider while selecting the right data warehouse for your business. Below is an example:copy table from 's3:///load/key_prefix' credentials 'aws_access_key_id=;aws_secret_access_key=' Options;You can load multiple files in parallel so that all the slices can participate. For the COPY command to work efficiently, it is recommended to have your files divided into equal sizes of 1 MB – 1 GB after compression.For example, if you are trying to load a file of 2 GB into DS1.xlarge cluster, you can divide the file into 2 parts of 1 GB each after compression so that all the 2 slices of DS1.xlarge can participate in parallel.Please refer to AWS documentation to get the slice information for each type of Redshift node.Using Redshift Spectrum, you can further leverage the performance by keeping cold data in S3 and hot data in the Redshift cluster. This way you can further improve your performance.In case you are looking for a much easier and seamless means to load data to Redshift, you can consider fully managed Data Integration Platforms such as Hevo. Hevo helps load data from any data source to Redshift in real-time without having to write any code.Athena – Ease of Data ReplicationSince Athena is an Analytical query service, you do not have to move the data into Data Warehouse. You can directly query your data over S3 and this way you do not have to worry about node management, loading the data, etc.Data Storage Formats Supported by Redshift and AthenaRedshift data warehouse only supports structured data at the node level. However, Redshift Spectrum tables do also support other storage formats ie. parquet, orc, etc.On the other hand, Athena supports a large number of storage formats ie. parquet, orc, Avro, JSON, etc. It also has a feature called Glue classifier. Athena is well integrated with AWS Glue. Athena table DDLs can be generated automatically using Glue crawlers

Infrastructure. Athena query DDLs are supported by Hive and query executions are internally supported by Presto Engine. Athena only supports S3 as a source for query executions. Athena supports almost all the S3 file formats to execute the query. Athena is well integrated with AWS Glue Crawler to devise the table DDLsRedshift Vs Athena ComparisonFeature ComparisonAmazon Redshift FeaturesRedshift is purely an MPP data warehouse application service used by the Analyst or Data warehouse engineer who can query the tables. The tables are in columnar storage format for fast retrieval of data. You can watch a short intro on Redshift here:Data is stored in the nodes and when the Redshift users hit the query in the client/query editor, it internally communicates with Leader Node. The leader node internally communicates with the Compute node to retrieve the query results. In Redshift, both compute and storage layers are coupled, however in Redshift Spectrum, compute and storage layers are decoupled.Athena FeaturesAthena is a serverless analytics service where an Analyst can directly perform the query execution over AWS S3. This service is very popular since this service is serverless and the user does not have to manage the infrastructure. Athena supports various S3 file-formats including CSV, JSON, parquet, orc, and Avro. Along with this Athena also supports the Partitioning of data. Partitioning is quite handy while working in a Big Data environmentRedshift Vs Athena – Feature Comparison TableFeature TypeRedshiftAthenaManaged or ServerlessManaged ServiceServerlessStorage TypeOver Node (Can leverage S3 for Spectrum)Over S3Node typesDense Storage or Dense ComputeNAMostly used forStructured DataStructured and UnstructuredInfrastructureRequires Cluster to manageAWS Manages the infrastructureQuery FeaturesData distributed across nodesPerformance depends on the query hit over S3 and partitionUDF SupportYesNoStored Procedure supportYesNoMaintenance of cluster neededYesNoPrimary key constraintNot enforcedData depends upon the values present in S3 filesData Type supportsLimited support but higher coverage with SpectrumWide variety of supportAdditional considerationCopy commandNode typeVacuumStorage limitLoading partitionsLimits on the number of databasesQuery timeoutExternal schema conceptRedshift Spectrum Shares the same catalog with Athena/GlueAthena/Glue Catalog can be used as Hive Metastore or serve as an external schema for Redshift SpectrumScope of ScalingBoth Redshift and Athena have an internal scaling mechanism.Get the best