AWS Lake Formation is a
service that allows you to quickly create a secure data lake. A data lake is a
centralised, controlled, and secure repository where you may keep all of your
data, both raw and processed for analysis. A data lake allows you to mix multiple
forms of analytics and break down data silos to acquire insights and make
better business decisions.
Defining data sources and the access and security policies you
want to apply is all it takes to create a data lake using Lake Formation. Lake
Formation then assists you in gathering and cataloguing data from databases and
object storage, moving it to your new Amazon Simple Storage Service (S3) data
lake, cleaning and classifying your data with machine learning algorithms, and
securing access to your sensitive data with granular controls at the column,
row, and cell levels. Your users will have access to a centralised data
catalogue that lists accessible datasets and how they should be used. They then
leverage these datasets with Amazon Redshift, Amazon Athena, Amazon EMR for
Apache Spark, and Amazon QuickSight, among other analytics and machine learning
services. Lake Formation builds on the capabilities available in AWS Glue.
AWS(Amazon Web Services) Interview
Questions and Answers
Ques. 1): Is there an API or a CLI available from Lake Formation?
Answer:
Yes. To incorporate Lake Formation capabilities into your bespoke
apps, Lake Formation provides APIs and a CLI. You can also use Java and C++
SDKs to combine your own data engines with Lake Formation.
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Ques. 2): What is a data lake, exactly?
Answer:
A data lake is a scalable central store for both organised and
unstructured data in huge numbers and types. Data lakes allow you to manage
your data over its entire lifecycle. Ingestion and classifying data from
various sources is the first stage in creating a data lake. Before analysis,
the data is enriched, merged, and cleansed. Direct searches, visualisation, and
machine learning make it simple to explore and evaluate data (ML). Traditional
data warehouses are supplemented by data lakes, which offer greater
flexibility, cost-effectiveness, and scalability for data acquisition, storage,
transformation, and analysis. The typical issues of building and maintaining
data warehouses, as well as constraints in the sorts of analysis that may be
performed, can be overcome utilising data lakes.
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Ques. 3): What is the AWS Lake Formation Storage API, and why
should I use it?
Answer:
The Lake Formation Storage API gives AWS services, ISV solutions,
and application developers a single interface to read and write data in the
data lake securely and reliably. To write data, the Storage API supports ACID
(atomic, consistent, isolated, and durable) transactions, which allow you to
reliably and consistently write data into Governed Tables, a new form of Amazon
S3 table. You can query data in Governed Tables and ordinary S3 tables guarded
with Lake Formation fine-grained permissions using the Storage API. Before
sending the filtered results to the requesting application, the Storage API
will automatically enforce permissions. Permissions for access are applied
uniformly across a variety of services and tools.
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Ques. 4): What exactly is the AWS Lake Formation?
Answer:
Lake Formation is a data lake service that makes it simple to
collect, clean, categorise, convert, and secure your data before making it
available for analysis and machine learning. Lake Formation provides a central
console from which you can discover data sources, set up transformation jobs to
move data to an Amazon Simple Storage Service (S3) data lake, remove duplicates
and match records, catalogue data for analytic tools, configure data access and
security policies, and audit and control access to AWS analytic and machine
learning services.
Lake Formation uses Zeppelin notebooks with Apache Spark to
automatically control access to the registered data in Amazon S3 using AWS
Glue, Amazon Athena, Amazon Redshift, Amazon QuickSight, and Amazon EMR to
ensure compliance with your established regulations. Lake Formation configures
the flows, centralises their orchestration, and allows you to monitor
transformation operations that span AWS services. You may configure and
maintain your data lake using Lake Formation instead of manually integrating
numerous underlying AWS services.
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Answers
Ques. 5): Can I utilise Lake Formation with third-party business
intelligence tools?
Answer:
Yes. You can connect to your AWS data sources using services like
Athena or Redshift using third-party business applications like Tableau and
Looker. The underlying data catalogue manages data access, so you can rest
certain that access to your data is authorised and controlled regardless of
whatever application you use.
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Answers
Ques. 6): How does Lake Formation de-duplicate my data?
Answer:
The FindMatches ML Transform from Lake Formation makes it simple
to locate and link records that refer to the same thing but lack a valid
identifier. Before FindMatches, data-matching problems were usually solved
deterministically by constructing a large number of hand-tuned rules. Behind
the scenes, FindMatches uses machine learning algorithms to learn how to match
records according to each developer's business requirements. FindMatches
selects records for you to categorise as matching or not matching, and then
utilises machine learning to generate an ML Transform. You can then use this
Transform to find matching records in your database, or you can ask FindMatches
to provide you with more records to label in order to improve the accuracy of
your ML Transform.
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Ques. 7): How does Lake Formation keep my information safe?
Answer:
Lake Formation safeguards your data by allowing you to define
granular data access policies that protect your data regardless of which
services are utilised to access it.
To use Lake Formation to consolidate data access policy
restrictions, disable direct access to your Amazon S3 buckets so that Lake
Formation handles all data access. Then, using Lake Formation, set up data
protection and access controls that are enforced across all AWS services that
access data in your lake. Users and roles can be configured, as well as the
data that these roles have access to, down to the table and column level.
S3 server-side encryption is now supported by Lake Formation
(SSE-S3, AES-265). Lake Formation additionally supports private endpoints in
your Amazon VPC and logs all activity in AWS CloudTrail, ensuring network
isolation and auditability.
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Ques. 8): What are Machine Learning Transforms?
Answer:
ML Transforms is a place where you can create and manage
machine-learned transforms. These ML Transforms can be used in ordinary AWS
Glue scripts once they've been constructed and trained. You choose an algorithm
(for example, the FindMatches ML Transform), then input datasets and training
samples, as well as tweaking parameters. These inputs are used by AWS Lake
Formation to create an ML Transform that can be integrated into a standard ETL
job workflow.
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Ques. 9): How can I turn an existing Amazon S3 table into a
regulated table?
Answer:
You can convert existing Amazon S3–based tables in the AWS Glue
Data Catalog to controlled tables by running the AWS Glue blueprint available
on the AWS Labs Github page. Using the AWS SDK and CLI, you can also create a
new governed table and edit the manifest information in Lake Formation. A list
of S3 objects and related metadata indicate the current status of your table in
the manifest information. You can also use AWS Glue ETL to read data from an
existing table and construct a Governed Table duplicate of it. This allows you
to migrate your applications and users to the Governed Table at your own pace.
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and Answers
Ques. 10): How does Lake Formation relate to other AWS services?
Answer:
Lake Formation manages data access for registered data that is
stored in Amazon S3 and manages query access from AWS Glue, Athena, Redshift,
Amazon QuickSight, and EMR using Zeppelin notebooks with Apache Spark through a
unified security model and permissions. Lake Formation can ingest data from S3,
Amazon RDS databases, and AWS CloudTrail logs, understand their formats, and
make data clean and able to be queried. Lake Formation configures the flows,
centralizes their orchestration, and lets you monitor the jobs.
AWS Simple Storage Service (S3)
Interview Questions and Answers
Ques. 11): What other options do I have for getting data into AWS
to utilise with Lake Formation?
Answer:
With AWS Snowball, AWS Snowball Edge, and AWS Snowmobile, you can
transport petabytes to exabytes of data from your data centres to AWS utilising
physical equipment. AWS Storage Gateway allows you to link your on-premises
apps directly to AWS. You can use AWS Direct Connect to create a dedicated network
link between your network and AWS, or Amazon S3 Transfer Acceleration to boost
long-distance global data transfers using Amazon's internationally spread edge
locations. Amazon Kinesis can also be used to import streaming data into S3.
Lake Formation Data Importers can be configured to run ETL processes in the
background and prepare data for analysis.
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Ques. 12): What is the relationship between Lake Formation and AWS
Glue?
Answer:
With AWS Glue, Lake Formation shares infrastructure such as
console controls, ETL code development and job monitoring, blueprints for
creating data import workflows, the same data catalogue, and a serverless
architecture. Although AWS Glue focuses on these operations, Lake Formation
includes all of AWS Glue's functionality as well as extra capabilities for
building, securing, and managing a data lake. For additional information, see
the AWS Glue features page.
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Ques. 13): How does Lake Formation sanitise my data using machine
learning?
Answer:
Lake Formation offers jobs that use machine learning methods to
deduplicate and connect records. Select your source, choose a desired
transform, and provide training data for the necessary changes to create ML
Transforms. The ML Transforms can be run as part of your regular data movement
procedures once they've been trained to your satisfaction.
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Ques. 14): What is the relationship between Lake Formation and AWS
IAM?
Answer:
Lake Formation works with IAM to automatically map authorised
users and roles to data protection policies maintained in the data catalogue.
You may also utilise Microsoft Active Directory or LDAP to federate into IAM
utilising SAML thanks to the IAM integration.
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Ques. 15): How does Lake Formation assist me in locating data for
my data lake?
Answer:
Lake Formation detects all AWS data sources to which it has access
thanks to your AWS IAM policies. It scans Amazon S3, Amazon RDS, and AWS
CloudTrail sources, identifying them as data that can be consumed into your
data lake using blueprints. Without your permission, no data is ever moved or
made accessible to analytic services. AWS Glue may also consume data from other
AWS services, such as S3 and Amazon DynamoDB.
Lake Formation may also use JDBC connections to connect to your
AWS databases as well as on-premises databases including Oracle, MySQL,
Postgres, SQL Server, and MariaDB.
Lake Formation guarantees that all of your data is documented in a
central data catalogue, allowing you to browse and query data that you have
authorization to see and query from a single location. Permissions can be
specified at the table and column level and are described in your data access
policy.
You can add labels (including business attributes like data
sensitivity) at the table or column level, as well as field-level comments, in
addition to the properties automatically provided by the crawlers.
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Interview Questions and Answers
Ques. 16): What types of issues does the FindMatches ML Transform
address?
Answer:
FindMatches solves record linkage and data deduplication issues in
general. When you're trying to find records in a database that are
theoretically the same yet have separate records, deduplication is required. If
duplicate entries can be identified by a unique key (for example, if products
can be uniquely identified by a UPC Code), this problem is straightforward, but
it gets exceedingly difficult when you have to execute a "fuzzy
match."
Record linkage is essentially the same as data deduplication,
however instead of deduplicating a single database, this phrase usually refers
to a "fuzzy join" of two databases that don't share a unique key.
Consider the difficulty of matching a large consumer database with a limited
database of known fraudsters. Both record linkage and deduplication
difficulties can be solved with FindMatches.
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Ques. 17): How does Lake Formation organize my data in a data
lake?
Answer:
You can use one of the blueprints available in Lake Formation to
ingest data into your data lake. Lake Formation creates Glue workflows that
crawl source tables, extract the data, and load it to Amazon S3. In S3, Lake
Formation organizes the data for you, setting up partitions and data formats
for optimized performance and cost. For data already in S3, you can register
those buckets with Lake Formation to manage them.
Lake Formation also crawls your data lake to maintain a data
catalog and provides an intuitive user interface for you to search entities (by
type, classification, attribute, or free-form text).
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Ques. 18): How does Lake Formation assist a data scientist or
analyst in determining what data they have access to?
Answer:
Lake Formation guarantees that all of your data is defined in the
data catalogue, providing you with a central area to browse and query the data
that you have access to. Permissions can be specified at the table and column
level and are described in your data access policy.
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Ques. 19): Why should I build my data lake with Lake Formation?
Answer:
Building, securing, and managing your AWS data lake is simple with
Lake Formation. Lake Formation automatically configures underlying AWS
security, storage, analysis, and machine learning services to meet with your
centrally set access policies. You can also monitor your jobs, data
transformation, and analytic workflows from a single console.
AWS Glue allows Lake Formation to handle data intake. Data is
automatically categorised, and the central data catalogue stores pertinent data
definitions, schema, and metadata. AWS Glue also cleans your data, removing
duplicates and linking entries across datasets before converting it to one of
several open data formats for storage in Amazon S3. You can create access
restrictions, including table-and-column-level access controls, and enforce
encryption for data at rest once your data is in your S3 data lake. You may
then access your data lake using a range of AWS analytic and machine learning
services. All access is controlled, monitored, and audited.
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Ques. 20): Can I utilise Lake Formation with my existing data
catalogue or Hive Metastore?
Answer:
You can import your existing catalogue and metastore into the data
catalogue using Lake Formation. To provide governed access to your data, Lake
Formation requires your metadata to be stored in the data catalogue.
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