![]() For more information about row-level security, see Overview of data filtering. Use row and cell-level security to protect sensitive data like Personal Lake Formation provides data filters that allow you to restrict access to a combination ![]() This process, commonly known as compaction, is performed in the background so that there is no performance impact on your production workloads while this is taking place.įor more information about the storage optimization features of Lake Formation, see Storage optimizations for governed tables. Lake Formation includes a storage optimizer that automatically combines small files into larger files to speed up queries by up to 7x. Small files creates additional overhead for analytics services and causes slower query See Matching Records with AWS Lake Formation FindMatches in the AWS Glue Developer Guide.Īnalytics performance can be impacted by inefficient storage of many small files thatĪre automatically created as new data is written to the data lake. Records across two databases.For more information about FindMatches, Machine learning transform that you can use to find duplicate records within a database or matching System will then learn your criteria for calling a pair of records a match and will build an Pizzeria” at “121 Main.” FindMatches will simply ask you to label sets of records as either “matching” or “not matching.” The Learning transform called FindMatches for deduplication and finding matching records.įor example, use FindMatches to find duplicate records in your database of restaurants, suchĪs when one record lists “Joe's Pizza” at “121 Main St.” and another shows “Joseph's Lake Formation helps clean and prepare your data for analysis by providing a machine Your data is transformed with AWS Glue and written in columnar formats, such as Lake Formation creates transformation templates and schedules jobs to prepare your data forĪnalysis. Lake Formation can perform transformations on your data, such as rewriting various dateįormats for consistency to ensure that the data is stored in an analytics-friendly fashion. Information about adding tables to the Data Catalog, see Managing Data Catalog Tables and Databases. This metadata so your users can quickly find the data they need to analyze. Information” and “European sales data.” Lake Formation provides a text-based search over Your data (at the table and column level) to define attributes, such as “sensitive You can also add your own custom labels to Users so they can discover available datasets. Lake Formation crawls and reads your data sources to extract technical metadata and creates a searchable catalog to describe this information for To import data from databases other than the ones listed above, you can create custom ETL Identify your target sources and provide accessĬredentials in the console, and Lake Formation reads and loads your data into the data lake. You can use Lake Formation to move data from on-premises databases by connecting with MariaDB, and Oracle databases running in Amazon RDS or hosted in Amazon EC2. With Lake Formation, you can import data from MySQL, PostgreSQL, SQL Server, It then imports the data to your new data lake and records the metadata in aĬentral catalog. ![]() Lake Formation reads the data and its metadata (schema) to understand the contents of the data ![]() Once you specify where your existing databases are and provide your access credentials, Import data from databases already in AWS
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