AWS Lake Formation
How it Works
It is centralized, curated, and password protected. You may get insights and lead smarter business decisions by combining multiple forms of analytics in a data lake to break down data silos and mix different types of analytics. Currently, there are numerous time-consuming procedures involved in setting up and administering data lakes. It entails loading data from a variety of sources, monitoring the data flows, configuring partitions, enabling encryption and managing keys, defining transformation jobs and monitoring their operation, organizing information in columnar format, deduplicating redundant data, and matching linked records, among other tasks. You must allow fine-grained access to datasets and track access over time across a broad range of analytics and machine learning (ML) tools and services after data has been deposited into the data lake.
Lake Formation makes it simple to create data lakes by simply specifying the data sources to be used as well as the access and security policies that will be applied to the lake. Once you’ve collected and catalogued data from databases and object storage, Lake Formation will assist you in moving the data into your new Amazon Simple Storage Service (S3) data lake, cleaning and classifying your data using machine learning algorithms, and securing access to your sensitive data with granular controls at the column, row, and cell levels. Customers and partners can search for and download datasets from a centralized data catalogue, which specifies accessible datasets and how they should be utilized. They then combine these datasets with a variety of analytics and machine learning technologies, such as Amazon Redshift, Amazon Athena, Amazon EMR for Apache Spark, and Amazon QuickSight, to achieve their desired results. It extends the capabilities of AWS Glue to create Lake Formation.
Lake Formation assists you in the creation, security, and management of your data lake. The first step is to discover existing data storage, whether they are in S3 or relational or NoSQL databases, and then move the data into your data lake. The data should next be crawled, catalogued, and prepared for analysis. Provide your users with safe self-service access to their data by allowing them to choose from among a variety of analytics providers. In addition to the services shown, other AWS services and third-party apps can access data through them. Lake Formation is responsible for managing all of the tasks depicted in the orange box and integrating them with the data stores and services depicted in the blue box.
Create data lakes quickly: Data Lake Formation allows you to move, store, catalogue, and clean data considerably faster than before. Lake Formation will automatically crawl your data sources and move the data into your new Amazon S3 data lake. The Lake Formation service organizes data in S3 around frequently used query words and into manageable chunks. Data is translated into formats like Apache Parquet and ORC for faster analysis. Deduplicate and find matching records (two entries that refer to the same object) are also available in Lake Formation, improving the data set overall.
Simplify the management of security: Lake Formation allows you to define and enforce table, column, row, and cell access controls for all users and services that access your data. Your policies are applied consistently across AWS services, removing the need to manually set security, storage, analytics, and machine learning policies across AWS services such as Redshift, Athena, AWS Glue, and EMR for Apache Spark. This saves time and provides uniform enforcement and compliance across services.
Self-service data access: Data lake Formation allows you to establish a data catalogue that lists all available datasets and who has access to them. This helps your users discover the right dataset to analyze, increasing their productivity. Lake Formation provides your analysts and data scientists with a catalogue of your data that is safeguarded by constant security enforcement. They may now analyze many datasets in a single data lake using EMR for Apache Spark, Redshift, Athena, AWS Glue, and Amazon QuickSight. Users can also mix services without transferring data across silos.
What does the AWS Lake Formation includes
Import data from existing databases: The data is scanned by AWS Lake Formation when you identify the location of your existing databases and submit your access credentials. Once the data is loaded, it captures the metadata in a central catalogue. Data from Amazon Relational Database Service (RDS) or Amazon Elastic Compute Cloud (EC2) databases can be imported into Lake Formation (EC2). Data can be loaded in bulk or incrementally.
Integrate data with other sources: Lake Formation may connect to on-premises databases via Java Database Connectivity (JDBC). Select your target sources and login credentials in the console, and Lake Formation will read and load your data into the data lake. Create custom ETL processes using AWS Glue to import data from databases other than those described above.
Data from other AWS services can be imported: Using the same method, you may import semi-structured and unstructured data from different S3 data sources into Lake Formation. The first step is to identify existing Amazon S3 buckets. Lake Formation reads the data and the schema included in the data by specifying an S3 path. Using the data lake Formation service, you may organize data from AWS CloudTrail, AWS CloudFront, Detailed Billing Reports, and AWS Elastic Load Balancing (ELB). Custom processes can also import data into the data lake using Amazon Kinesis or Amazon DynamoDB.
organize and label your data: When users search for datasets, Lake Formation provides a searchable library of technical metadata (such as schema definitions) extracted from your data sources. Lake Formation can crawl and read your data to obtain technical metadata (such as schema definitions). Custom labels can be applied to your data (table and column level) to denote elements like “important information” and “European sales data.” Lake Formation allows users to search for data utilizing text-based search over metadata, allowing them to find information quickly.
Data transformation: Lake Formation can perform transformations on your data, such as rewriting several date formats to ensure consistency. Amazon data lake Formation develops transformation templates and organizes jobs to prepare your data for analysis. Your data is translated with AWS Glue and stored in columnar formats like Parquet and ORC. Less data must be read for analysis when data is sorted into columns rather than rows. AWS Glue and Apache Spark allow you to construct custom transformation jobs for your organization or project.
Enhanced partitions: Lake Formation optimizes data partition in Amazon S3 to improve performance and reduce costs. Many unprocessed raw data files may be loaded into partitions that are too small (requiring extra reads) or too large (requiring no more reads) (reading more data than needed.) Lake Formation organizes your data by size, time period, and/or relevant keys. Quick scans and parallel, dispersed readings benefit the most frequently used queries.
Enforce encryption: Lake Formation uses Amazon S3’s encryption to secure your data lake. This solution provides server-side encryption utilizing AWS Key Management Service keys (KMS). S3 encrypts data in transit while replicating across Regions and allows you to use separate accounts for the source and destination regions to protect against malicious insider deletions. These encryption features secure your data lake, allowing you to focus on other responsibilities.
Manage access controls: Lake Formation centralizes data access control for your data lake. You can set security policies for the database, table, column, row, and cell levels. All AWS Identity and Access Management (IAM) users and roles are subject to these policies. Lake Formation secures data within Amazon Redshift Spectrum, Amazon Athena, AWS Glue ETL, and Amazon EMR for Apache Spark.
Set up audit logging: Amazon data lake Formation delivers extensive audit logs with CloudTrail to track access and policy compliance. Using Lake Formation, you can track data access across analytics and machine learning platforms. This shows which users or roles tried to access which data, when, and with which services. Audit logs are accessible via the CloudTrail APIs and console, much like regular CloudTrail logs.
Regulated tables: Accurately inject data into several tables on Amazon S3 using ACID transactions. All users see the same data because Governed Table transactions automatically resolve conflicts and mistakes. Use Amazon Redshift, Amazon Athena, and AWS Glue transactions to query Governed Tables.
Data meta-tagging for business: By adding a field to table properties as a custom attribute, you can designate data owners like data stewards and business units. Your owners might add commercial information to the technical metadata to better define data uses. Using amazon data lake Formation security and access controls, you may define appropriate use cases and data sensitivity levels.
Allow self-service: Lake Formation enables self-service data lake access for a number of analytics use cases. Permissions on tables defined in the central data catalogue can be specified. Multiple accounts, groups, and services share the same data catalogue.
Find data for analysis: Lake Formation allows users to search and filter datasets stored in a central data catalogue using text searches online. They can look for data by name, content, sensitivity, or any other custom label you set.
Combine analytics to gain greater insight: Provide your analytics users with direct access to data using Athena for SQL, Redshift for data warehousing, AWS Glue for data preparation, and EMR for Apache Spark–based big data processing and ML (Zeppelin notebooks). You can easily mix analytical approaches on the same data by pointing these services to amazon data lake Formation.
Database, table, column and tag-based access controls are free with AWS Lake Formation. Accurately changing many tables while preserving a consistent view for all users is possible with Governed Tables. Managing concurrent transactions and reverting to a previous table version requires storing transaction metadata. Pay for transaction requests and metadata storage. The Lake Formation Storage API examines data in Amazon S3 and adds row and cell filters before returning results to applications. This filtering is not free.
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