The American Journal of Interdisciplinary Innovations
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10.37547/tajiir/Volume07Issue07-03
OPEN ACCESS
SUBMITED
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ACCEPTED
18 June 2025
PUBLISHED
07 July 2025
VOLUME
Vol.07 Issue07 2025
CITATION
Vrushali Parate. (2025). Hadoop To Bigquery: Migrating Automotive Data
Lakes Without Downtime. The American Journal of Interdisciplinary
Innovations and Research, 7(07), 16
–
27.
https://doi.org/10.37547/tajiir/Volume07Issue07-03
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Investi
Hadoop To Bigquery:
Migrating Automotive
Data Lakes Without
Downtime
Vrushali Parate
Department of Computer Science and Engineering, University of
Bridgeport, Bridgeport, CT, 06604, USA
Abstract:
The automotive industry is undergoing a
tremendous increase in data generation, mostly driven
by advancements in vehicle technology, connectivity,
and autonomous driving features. The Apache Hadoop
data lake was adopted by companies to store and
analyze the huge volume, velocity, and variety of
automotive data. However, with technological
advancement and the need for real-time analytics,
operational complexity, scalability, and cost efficiency,
Apache Hadoop-based data lakes started presenting
challenges. Google BigQuery, on the other hand, is a
fully managed, serverless data warehouse and analytics
platform that offers a good alternative with its scalable
architecture, high performance, ease of use, and
integration with advanced analytics and machine
learning services. Migrating this massive amount of
automotive data from Hadoop to BigQuery needs
careful planning and execution, especially while making
sure there are fewer disruptions with the ongoing
business and avoiding downtime. This paper explores
the typical architecture and use case of Hadoop-based
data lakes in the automotive sector, explores BigQuery
as an alternative option while also considering its
benefits, and analyzes various strategies and methods
for a seamless migration. Further, it delves into
techniques and best practices for achieving zero
downtime during the migration of large automotive
datasets, addresses the specific challenges and
considerations involved in handling automotive data’s
unique characteristics, examines relevant case studies
of successful migrations, investigates methods for
ensuring data consistency and integrity, and researches
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approaches to optimize data processing and analytics
workflows on BigQuery post-migration.
Keywords:
Data Lake, Hadoop, BigQuery, Automotive
Industry, Migration
1.
INTRODUCTION
The automotive industry is undergoing substantial
growth with the advancement of technology. Modern
vehicles produce tremendous streams of data from
telematics, sensors, connected vehicles, and over-the-
air (OTA) updates, which in turn contribute to
exponential data growth. In addition to connected
vehicle data, automobile manufacturers and their tier 1
and tier 2 suppliers use dozens of IoT sensors in their
manufacturing equipment to track procurement,
production, and shipping processes, which is putting
more fuel on the data explosion. This sudden growth is
creating unprecedented data challenges, with electric
vehicles (EVs) leading the way. Global Electric vehicle
(EV) sales reached 17.1 million units in 2024, which is a
25% increase over 2023. The U.S. and Canada registered
a 9% year-over-year growth in EV sales, whereas China's
growth was 40% [1
].
The rapid speed at which this data is being produced in
different formats makes it necessary to have robust data
management solutions. Data lakes have become a key
part of how automotive companies manage their data.
A data lake is a centralized repository that can store
structured, semi-structured, and unstructured data in its
raw format without requiring any transformation [2].
This flexibility to store the data in any format is
especially important for the automotive industry since
the data gets generated from various sources, such as
sensors, telematics, manufacturing logs, part sales
revenue, software updates, and multimedia. Data lakes
are used by automotive manufacturers for numerous
applications, such as predicting equipment maintenance
needs, enhancing vehicle design and performance, fleet
optimization, personalization, sales, and marketing. By
providing a single platform for all enterprise data, data
lakes free data silos and make it easier to perform end-
to-end analytics, something that is important for getting
an
overview
of
automobile
industry
vehicle
performance, customer behavior, and market trends.
Apache Hadoop was traditionally used as a data lake in
the automotive industry and has played a significant role
in the evolution of big data management. Cost-
effectiveness, scalability, and fault-tolerant architecture
were the reasons for adopting Apache Hadoop as a data
lake by organizations. Hadoop enabled automotive
organizations to store and process voluminous data
using commodity hardware. However,
with the
expansion of technology and business needs, Hadoop-
based data lakes have encountered limitations and
challenges
[3]. One of the limitations that Hadoop
encountered was not being able to process data in real
time. Hadoop was originally built for batch processing
through the MapReduce framework, which makes it less
ideal for scenarios that demand quick insights, like
processing real-time data from connected vehicles.
The
surge in the use of connected vehicles, electric mobility,
and autonomous systems has increased the importance
of real-time data processing in the automobile industry.
Applications including monitoring the battery health,
real-time data analysis for autonomous driving
algorithms, and predictive maintenance for critical
vehicle components demand low latency and quick
response time. However, Hadoop’s batch
-oriented
architecture,
with
the
added
complexity
of
incorporating real-time layers, falls short in meeting
these requirements. Moreover, maintaining a Hadoop
ecosystem needs significant operational overhead, and
a dedicated resource is required to perform regular
system updates, performance tuning, and security
configurations.
These challenges may have led the
organizations to explore cloud-native alternatives that
offer managed infrastructure, seamless scalability, and
integrated real-time analytics capabilities. Cloud
platforms like Google BigQuery are increasingly
preferred for their ability to support modern data
processing needs.
Google BigQuery stands out as a strong alternative to
Hadoop for handling and analyzing automotive data. As
a fully managed, serverless data warehouse, it removes
the need for infrastructure setup and management,
letting automotive teams focus on using their data
effectively [4]. BigQuery can process petabyte-scale
datasets quickly using its distributed engine, making it
ideal for the large data volumes generated in this
industry. It also supports real-time streaming, which is
critical for applications like connected vehicles
—
something Hadoop struggles with. Its pay-as-you-go
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pricing and efficient storage can be more cost-effective
than maintaining traditional Hadoop systems [4].
BigQuery also includes built-in tools for security and
data governance, which are essential when working with
sensitive vehicle and customer data [4]. For automotive
companies, BigQuery offers a scalable, flexible, and
faster path toward smarter analytics and innovation.
The primary
research problem this paper aims to solve
is the need for an end-to-end approach to migrate
automotive data lakes from on-premises Apache
Hadoop environments to Google BigQuery with zero
downtime. This migration is critical for the automotive
organizations to upgrade the data infrastructure by
overcoming the shortcomings presented by Hadoop and
leveraging the robust capabilities of BigQuery without
disrupting existing operations.
This research paper aims to explore the challenges and
opportunities involved in migrating automotive data
lakes from Hadoop to Google BigQuery. It begins by
analyzing the specific limitations of Hadoop in managing
the growing volume, speed, and complexity of
automotive data. The paper then explores the features
of Google BigQuery that make it particularly suitable for
this domain, such as its real-time processing capabilities,
scalability, cost-effectiveness, and built-in data
governance. Various migration strategies, including lift-
and-shift, phased approaches, hybrid models, and full
re-platforming are discussed to evaluate their suitability
for different scenarios. Special attention is given to best
practices that enable zero downtime during migration,
which is especially critical for real-time data streams
from connected vehicles. Real-world case studies from
the automotive sector or related industries are
examined to offer practical lessons. The paper also
compares the traditional Hadoop-based architecture
with BigQuery’s cloud
-native alternative, highlighting
the advantages of the latter in terms of performance
and maintainability. Finally, performance benchmarks
and cost comparisons between the two platforms are
presented to provide a data-driven assessment of
BigQuery’s value. Through these discussions, the paper
serves as a comprehensive guide for automotive data
professionals planning a seamless, zero-downtime
migration to a modern, scalable data platform.
2.
Hado
op Data Lake in the Automotive Industry
Figure. 1 represents a typical architecture of Hadoop in
the automotive industry. It contains HDFS (Hadoop
Distributed File System) as the primary storage layer,
providing a scalable and fault-tolerant repository for
large datasets [5]. YARN (Yet Another Resource
Negotiator) typically serves as the cluster resource
manager, which allocates system resources to various
applications and jobs running on the Hadoop cluster [6].
From Figure. 1, the third layer represented the data
processing framework layer, such as MapReduce and
Apache Spark. MapReduce provides a programming
model for processing large datasets in parallel, while
Apache Spark provides faster processing due to the
concurrent use of batch and stream processing [7].
Figure. 1 Hadoop Architecture
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The automotive industry generates a vast amount of
data from vehicle sensors, software updates,
multimedia, and IoT [8].
Sensor data from vehicles is
ingested and analyzed to identify potential issues and
predict failures. The development of autonomous
driving depends on data lakes to store a variety of
voluminous data generated from sensors like cameras
and radar, which are important for training and
validating autonomous algorithms. Connected vehicle
data, sales, and service data help to understand
customer preferences, personalize marketing efforts,
and improve product development. Furthermore,
predictive maintenance for manufacturing equipment is
an important use case, where sensor data from the
production line is analyzed to anticipate potential
failures and minimize downtime.
Even though the Hadoop data lake offers diverse
applications in the automotive industry, it began to
show limitations in this data-dominated world. With this
increase in volume of data, scalability can be a major
concern. Hadoop architecture may struggle to handle
real-time analytics efficiently [9]. Operational costs
associated with managing Hadoop clusters and the need
for specialized experts can be substantial. Furthermore,
ensuring data quality and establishing robust
governance mechanisms in Hadoop can be challenging.
Security issues and the difficulty of setting up strong
protection systems in large Hadoop setups are major
concerns for the automotive industry, especially
because they deal with sensitive vehicle and customer
data.
The automotive industry depends heavily on Hadoop
data lakes to manage a wide range of data-driven tasks,
highlighting the importance of having a single place to
store different types of data. However, Hadoop is
starting to show its limits, especially when it comes to
handling fast-moving data and the need for more
flexible, cost-effective solutions. As managing large and
complex automotive data becomes harder and the need
for better data quality and governance grows, many
companies are now looking to move to platforms like
Google BigQuery. These newer tools are built to handle
these challenges more efficiently.
3.
Google BigQuery and Its Capabilities
Google BigQuery is a fully managed, serverless data
warehouse that provides a scalable and cost-effective
solution for analyzing data [4]. From Figure. 2, we can
see that Google BigQuery contains separate compute
and storage layers, which allows each layer to scale
independently as needed. The storage system, called
Colossus, is a global distributed file system that securely
stores massive amounts of data [4]. Data is kept in a
columnar format known as Capacitor, which speeds up
analysis by allowing specific columns to be accessed
without scanning entire rows [4]. BigQuery uses the
Dremel query engine and a massively parallel processing
(MPP) system to run SQL queries quickly, often
processing terabytes of data in just seconds. A high-
speed internal network connects storage and compute
resources, ensuring fast and efficient data transfer [4].
Figure. 2 Google BigQuery Architecture
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BigQuery provides several features that make it
especially useful for
the automotive industry’s data
needs. It can easily scale to handle the growing amounts
of data produced by connected vehicles without
needing manual setup or capacity planning [4]. Because
it's serverless, companies don’t need to manage any
servers or infrastructure, which allows their teams to
focus more on analyzing data [4]. BigQuery also supports
real-time streaming, so data from vehicles and IoT
devices can be analyzed immediately. It works with
various file formats like Avro, Parquet, JSON, and CSV,
which is helpful given the range of data sources in
automotive systems [4]. BigQuery integrates with
Google Cloud's machine learning tools like Vertex AI and
includes built-in machine learning (BigQuery ML), so
teams can build models for things like predictive
maintenance or detecting fraud directly within the
platform. Its pay-as-you-go pricing is often more
affordable than maintaining a Hadoop system,
especially with tools like clustering and partitioning to
make queries more efficient [4].
Compared to traditional data warehouses, BigQuery
stands out for its scalability and flexibility, making it well-
suited for the large and varied datasets commonly found
in the automotive industry [4]. Unlike Hadoop, which
requires manual setup and ongoing maintenance,
BigQuery’s serverless setup simplifies operations and
lowers administrative effort. Its real-time data
streaming also gives it an edge over Hadoop’s batch
processing, especially for time-sensitive use cases like
vehicle telemetry and diagnostics [4]. While Hadoop is
effective for storing large amounts of unstructured data,
BigQuery is better optimized for running fast analytical
queries and includes built-in tools for data governance
and security. Its architecture is purpose-built to address
many of the challenges seen with Hadoop, offering the
scalability, low maintenance, and real-time capabilities
needed for the automotive industry's evolving analytics
demands. The built-in machine learning tools further
support the development of intelligent solutions
without requiring separate platforms [4].
The integration of machine learning capabilities
provides an added advantage to the automotive
industry. It allows data professionals to query or build
ML models and deploy them quickly without the need to
move the data to a separate platform, which is often the
case with Hadoop. This built-in tool simplifies the
process and helps speed up the process in the AI-driven
world.
Table 1 shows an overview of the comparison between
Hadoop and Google BigQuery based on key features that
are important for building and managing the automotive
data lakes. It looks at features such as scalability, real-
time processing, ease of use, cost, machine learning
support, and more. The table also explains how these
differences are related to the automotive use cases,
such as connected vehicles, data analysis, and predictive
maintenance. This helps highlight why a platform like
BigQuery might be better suited for the modern needs
of the automotive industry.
Table 1: Comparison of Hadoop and BigQuery for Automotive Data Lakes
Feature
Hadoop
Google BigQuery
Why It Matters for
Automotive
Scalability
Scales well with
added servers
Automatically scales
as needed
Both handle large vehicle
and operational data, but
BigQuery does it with less
effort.
Real-time
Needs an extra tool
Real-time streaming
Essential for connected
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Processing
like Spark Streaming
built-in
cars and real-time vehicle
diagnostics.
Ease of Use
Complex setup and
upkeep
Fully managed and
serverless
Reduces IT workload -
teams focus more on
insights, not infrastructure.
Cost
Lower upfront (on-
prem servers)
Pay for what you use
(storage + compute)
BigQuery can be more
budget-friendly for data-
heavy tasks and has
predictable pricing.
Data Formats
Works with most
types of data
Supports formats
like Avro, Parquet,
JSON
Ideal for handling varied
automotive data from
sensors, logs, and more.
Machine
Learning
Needs separate tools
(e.g., Spark ML)
Built-in ML tools
(BigQuery ML,
Vertex AI)
Makes building models for
maintenance, fraud, or
customer targeting easier.
Query
Performance
Slower for heavy
analytics
Very fast on large
datasets
Let analysts get quick
insights - great for
operations and field
teams.
Data Governance
Mostly manual setup
Built-in controls with
Dataplex integration
Supports privacy,
compliance, and cleaner
data management.
4.
Exploring Various Methods of Migration
Migrating automotive data from one data lake to
another requires proper planning and strategy that
aligns well with the organization's needs, resources, and
tolerance for disruption. There are several techniques
available, which have their own advantages and
disadvantages:
4.1
Lift and Shift:
This is one of the most common
strategies, which involves moving existing Hadoop
infrastructure, including HDFS data and processing
tools like Spark and Hive, to Google Cloud using
Google Cloud Dataproc.
Dataproc is a managed
service that supports Hadoop and Spark workloads
allowing teams to migrate their systems with fewer
changes to their architecture. While this strategy
involves minimal work, it might not fully leverage
the benefits and cost efficiencies of BigQuery, as
data processing remains in the Hadoop ecosystem
[10].
4.2
Phased Migration:
This strategy is performed in
phases by dividing the data and workloads based
on business units or datasets and then migrating
from Hadoop to BigQuery, maintaining the Hadoop
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environment for ongoing operations. While this
migration takes place in phases, it is also crucial to
test and validate data in BigQuery, which leaves
less room for widespread disruption. This strategic
migration enables teams and businesses to learn
and adapt to BigQuery while ensuring continuity in
the business before a full cutover. This migration
can be started with less critical workloads,
gradually
moving
towards
more
complex
workloads as the confidence in working with the
BigQuery environment increases [11].
4.3
Hybrid Approaches:
Some companies may adopt
this approach, where both Hadoop and BigQuery
are utilized for different workloads or datasets
depending on the use case. Hadoop can be used for
data storage purposes, while BigQuery can be used
to perform advanced analytics or high-
performance workloads. Similar to phased
migration, this helps companies to slowly adopt
new data platforms such as BigQuery but
simultaneously introduces more complexity
related to managing two different platforms, which
in turn can be resource-consuming [
12
].
4.4
Replatforming/Rearchitecting:
This is a more
detailed strategy that involves rearchitecting data
models, optimizing processing pipelines, and
adapting storage formats to align with BigQuery’s
architecture. Typically, this includes migrating data
from HDFS to Google Cloud Storage, followed by
loading it into BigQuery for advanced analytics and
machine learning use cases. This would offer long-
term value for achieving better performance, is
cost-effective, and offers great flexibility in scaling
data operations [
10
].
5
.
Step-by-Step Process for Planning and Executing
Data Migration
Proper planning and execution steps are important to
follow, irrespective of the approach followed by the
automotive companies in terms of data migration. This
involves several key phases:
5.1
Assessment and Planning:
This is the first and most
important step in data migration. This involves
understanding the data in terms of volume,
workloads, types, governance policies, and
dependencies present in the existing Hadoop
ecosystem. This step often includes a proof-of-
concept (POC), which involves validating the
migration strategy chosen and BigQuery’s potential
with a sample dataset. Clear goals and success
metrics must be defined by automotive companies
at this step of migration, which helps in proper
resource allocation. Furthermore, appropriate
Google Cloud services for different types of
workloads need to be selected. For example, batch
processing ETL jobs might be targeted for Dataproc
and BigQuery, while streaming workloads using
Kafka might be designated for Pub/Sub and
Dataflow. Data warehousing workloads using Hive
might be directly mapped to BigQuery.
5.2
Extraction, Transformation, and Loading of Data:
After the completion of the first step, it is time to
perform ETL of data. Irrespective of the migration
strategy, this is a crucial step in the migration
process. For extracting the data from the Hadoop
Distributed File System (HDFS), several tools are
available to perform this action, among which the
most commonly used tool is the Hadoop Distcp
(Distributed Copy) utility [13]. It supports
distributed copying of large datasets across the
environment and can be used to migrate historical
data. The Google Cloud Storage connector for
Hadoop can also be used by companies to transfer
data between Hadoop clusters and Google Cloud
Storage (GCS) directly [
14].
For handling large-scale
migration, Google’s Transfer Service may also be
used. For incremental loads and ongoing data
synchronization, the gsutil command-line tool can
be used. Some level of data transformation is often
required, depending on the migration strategy and
BigQuery’s specific requirements [15]. Thi
s could
potentially include adjusting schemas, cleaning
datasets, and enriching the data to improve
compatibility and performance. Tools like Google
Cloud Dataflow can help build scalable pipelines to
automate and manage these transformation tasks.
Once the data is transformed, it is time to load the
data into Google BigQuery. Typically, historical
data, which is large in size, is loaded in batches,
while real-time data from software updates or
connected vehicle data is ingested using Google
BigQuery’s streaming systems. Google’s BigQuery
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Data Transfer Service can automate and streamline
data loading directly from sources like Google
Cloud Storage, which helps in reducing manual
efforts and complexity [15].
5.3
Workflow and Application Migration:
After
migrating data,
it is essential to migrate
existing
workflows and applications running on Hadoop.
Apache Spark workloads running on Hadoop can be
migrated to Dataproc. This may also involve
rewriting scripts in SQL that were earlier in HiveQL
or re-
architecting applications using BigQuery’s
features and API. Workflow orchestration tools like
Apache Oozie, commonly used in Hadoop
environments, can be migrated to Google Cloud’s
Cloud Composer (Apache Airflow) [15].
5.4
Testing and Validation:
With the entire migration
process, it is important to test and validate data to
maintain data integrity, accuracy, and quality. This
includes comparing data between the Hadoop
system and the Google BigQuery environment and
testing the performance and reliability of the
migrated analytics and reporting capabilities.
Automated data validation tools can be useful in
ensuring data synchronization and accuracy.
5.5
Cutover and Go-Live:
After completion of the
above step and making sure the data is accurate
and reliable for use by the teams, the final
transition takes place from the Hadoop data lake to
Google BigQuery. This step is carefully planned to
reduce disruption and redirect the traffic to
BigQuery while decommissioning the Hadoop
systems. A rollback plan should also be in place in
case of any unexpected issues taking place during
the final cutover.
While this migration takes place from Hadoop to
BigQuery, there are certain considerations one needs to
apply to different Hadoop components. For HDFS data,
the primary method involves copying the data to GCS
using tools like DistCp or gsutil. Hive metadata and
schemas need to be migrated to BigQuery’s data
catalog, which can sometimes be done using tools that
understand both Hive and BigQuery metadata formats.
Spark applications often need to be adapted to run on
Dataproc, which might involve changes in configuration
or code [15]. Oozie workflows can be migrated to Cloud
Composer, which provides a similar workflow
orchestration capability using Apache Airflow. For real-
time streaming data ingested via Kafka, the migration
might involve setting up Google Cloud’s Pub/Sub as the
message broker and using Dataflow for stream
processing and loading into BigQuery. Each of these
component-specific
migrations
requires
careful
planning and execution to ensure a seamless transition
[15].
6
.
Achieving Zero Downtime: Best Practices and
Techniques
The
following are some of the best techniques and
practices that would help in achieving zero downtime
during migration.
6.1
In-depth Analysis of Techniques for Continuous
Data Availability:
Migrating automotive data,
which supports important operational and
analytical processes, requires a strategy that
guarantees continuous data availability with
reduced disruptions to daily business processes. To
achieve zero downtime during the transition
process from Hadoop to BigQuery, organizations
must plan thoroughly and adopt special techniques
designed to maintain seamless operations
throughout the migration process.
6.1.1
Blue/green Deployment
: This is another strategy
used to minimize downtime for the migration. It
involves setting up and running two identical
environments: the old system, Hadoop (blue), and
the new system (green) in parallel [16]. Once the
new environment - Google BigQuery, is tested and
validated, the traffic can then be redirected from
the blue environment to the green environment.
This leaves enough time for the new environment
to be thoroughly tested in a production-like setting
before the actual transition takes place. In case of
any unexpected issues, the traffic can always be
diverted to the blue environment until the issues in
the green environment are fixed and tested,
ensuring minimal downtime. This method provides
a safe and controlled way to migrate to BigQuery
and roll back quickly, just in case [16].
6.1.2
Canary Deployment
: This strategy is similar to
blue/green deployment [16]. Canary releases are
the process that can be performed in small portions
by moving a small part of the application traffic to
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the BigQuery environment, monitoring the
performance, and identifying the potential issues
before full transition [16]. This allows the industry
to test BigQuery with a small amount of their
workload and user base, which gives early warnings
if there could be potential issues. If this strategy
ensures success, gradually the traffic to BigQuery
can be increased until the completion of migration.
This approach reduces the risk of large-scale
outages by allowing early issue detection before
full migration takes place.
6.1.3
Data Replication Strategies:
Using strong data
replication tools and strategies is another crucial
step toward achieving a zero-downtime migration.
Tools like Cirata Data Migrator for Hadoop
are
specifically built to enable continuous, real-time
replication of both data and Hive metadata from
on-premises HDFS to cloud storage solutions like
Google Cloud Storage, which BigQuery can then
access directly [17]. These tools typically perform
an initial one-time migration of the existing data,
followed by ongoing replication of any new or
updated data. This approach ensures that the
BigQuery
environment
stays
continuously
synchronized with the live production system. By
replicating the data in real-time, teams within the
organization can reduce downtime during the
transition. Once the migration is complete and
verified, the switch to BigQuery can happen
smoothly, without any major interruptions to
business operations.
6.2
Specific Challenges and Considerations for
Automotive Data Migration:
With the focus on
zero downtime on migrating the automotive data
lake, it requires careful attention to the unique
characteristics of automotive data. The high-
volume data streams generated by connected
vehicles make it critical to use BigQuery’s
streaming ingestion feature to ensure continuous,
real-
time data capture without any loss. BigQuery’s
scalable storage is also essential for managing the
voluminous sensor data and telemetry from
vehicles and manufacturing processes without
compromising performance. Maintaining data
consistency between the Hadoop and BigQuery
environments is important, especially for critical
automotive applications like safety systems,
predictive maintenance, and autonomous driving
development. To achieve this, teams within the
automotive industry must implement strong data
validation processes and use techniques such as
dual-write patterns and continuous replication to
ensure data reliability during migration. In addition,
careful planning is needed when migrating data
processing pipelines to BigQuery. These pipelines
must be adapted to handle the specific formats,
and processing demands of automotive data, all
while ensuring that downstream applications
continue to function smoothly without disruptions.
7
.
Case Studies of Successful Data Migration
Several case studies highlight successful migrations of
data lakes to cloud-based solutions, which can provide
valuable insights for automotive organizations. One such
case study is about a leading telematics provider,
Geotab [18]. It leveraged Google BigQuery to capture
and analyze telematics data from over 1.4 million
vehicles; processing more than 3 billion data records
every day. Their ability to analyze raw sensor data in just
5 to 10 seconds underscores BigQuery’s capability to
handle the high velocity and volume of automotive data
[18].
Uber, operating one of the world's largest Hadoop
installations, managing over an exabyte of data,
embarked on a strategic migration of its batch data
analytics and machine learning training stack to Google
Cloud Platform (GCP). The primary driver for this move
was to enhance scalability, streamline operations, and
eventually leverage the benefits of cloud-native services
[19].
Uber's initial migration strategy involved a "lift and shift"
approach, utilizing Google Cloud Storage (GCS) for their
data lake storage and migrating the remainder of their
data processing stack to GCP's Infrastructure as a Service
(IaaS) [10]. This allowed them to replicate their existing
on-premises environment on GCP with minimal
disruption to ongoing workflows.
To facilitate this complex transition, Uber developed
internal tools such as DataMesh, which helps manage
cloud infrastructure and enforce data governance, and a
Path Translation Service (PTS) to seamlessly map on-
premises HDFS paths to their corresponding cloud-
based locations [20].
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As of a certain point in their migration journey, Uber had
successfully moved over 160 petabytes of data to GCS
and was running more than 19% of its analytical
workloads on the GCP infrastructure. This migration
positions Uber to take full advantage of GCP's elasticity
and performance benefits in the future, ultimately
creating a more agile, secure, and cost-efficient data
ecosystem [20].
These case studies show successful Hadoop-to-cloud
migration, which often employs a strategic phased
approach. These allow incremental progress, validation
at each stage, and reduced risk.
8
.
Ensuring Data Consistency and Integrity During and
After Data Migration
Ensuring data consistency and integrity during and after
data migration from Hadoop to BigQuery is important to
maintain data accuracy and reliability for business
operations. This involves the implementation of robust
data validation and cleansing procedures at every step
of the migration of automotive data in the BigQuery
environment. This is an ongoing, proactive process to
reduce errors and guarantee reliable data. Several
methods can be implemented and used to maintain data
accuracy at various stages of migration.
Before starting the migration, data profiling should be
performed to examine the quality and characteristics of
the data in the Hadoop data lake, identifying any
inconsistencies, errors, duplicates, or missing values
that could cause issues in the later process [21]. The next
step in this process should be to perform data cleaning
so that these identified issues can be corrected, ensuring
only accurate and reliable data is migrated to BigQuery.
Implementing data validation protocols is one of the
most important steps in the process of ensuring data
consistency to help detect any discrepancies between
the source and destination data [21]. Checksums or hash
comparisons are the techniques used to verify the
integrity of the transferred data to ensure no data is lost
or corrupted during the migration process [21]. To
ensure the correctness and completeness of the data
migrated to the target system, a data reconciliation and
comparison process is performed between Hadoop and
BigQuery. For regular data validation, automated scripts
can be written and executed throughout the migration
process, which would quickly help in identifying any
inconsistencies or mismatches in the data migrated [21].
Post-migration, it is essential to implement ongoing data
quality monitoring and governance processes to
maintain data integrity in the long term [21]. This
includes processes such as setting up automated data
quality checks, monitoring data pipelines for errors, and
implementing data governance policies to ensure that
data remains accurate and reliable over the period.
Regular audits and validation checks should also be
performed to identify and address any potential issues
that may arise after the migration [21].
9
.
Optimizing Data Processes and Analytics in BigQuery
Optimizing data processes, workflows, and analytics
post-successful migration of automotive data is
important to fully leverage and utilize the features and
functions provided by BigQuery to achieve optimal
performance while being cost-efficient.
Designing data models and schemas is a fundamental
step. Expensive JOIN operations can be replaced by
nested and repeated fields. Using appropriate data
types, such as INT64 for join keys, can help query
performance and can be storage-efficient. Partitioning
large tables by date or other relevant columns, such as
vehicle identification number (VIN), can improve query
performance and reduce costs by allowing BigQuery to
only scan the necessary partitions.
Optimizing SQL queries is another important aspect of
maximizing BigQuery’s performance. Best practices,
such as using specific columns in SELECT statements,
should be enforced instead of SELECT *, which can
reduce the amount of data processed. Using proper
WHERE conditions to filter data early in the query
execution plan can help reduce cost and processing time
to a great extent. Another best practice to improve
query performance would be to use partitioning and
clustering on frequently queried columns to process
only relevant subsets of the data.
BigQuery offers advanced analytics and machine
learning capabilities, such as BigQuery ML, which allow
users to create and train machine learning models using
SQL queries within BigQuery, reducing the need for a
separate machine learning platform [22]. Geospatial
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analysis functions can be used to analyze location-based
data from connected vehicles for applications like route
optimization and traffic analysis. The BigQuery BI engine
is a fast, in-memory analysis service that can be used to
accelerate business intelligence and reporting on
BigQuery data, providing sub-second query response
times for interactive dashboards and reports. Utilizing
these features can unlock significant value from the
migrated automotive data lake [22].
10
.
CONCLUSION
This research was able to explore the process of
migrating automotive data lakes from Hadoop to Google
BigQuery while being able to focus on achieving zero
downtime. From the above analysis, it is known that
Hadoop has served as a traditional data lake for
managing voluminous data generated by the
automotive sector, but its limitations in scalability, real-
time processing, and complexity are becoming more
evident. On the other hand, Google BigQuery, with its
seamless architecture, independent scaling, and real-
time streaming capabilities, presents a good alternative
that directly addresses the shortcomings.
Considering this migration for automotive companies,
several practical steps can be offered. Starting with a
phased migration to help minimize risk by allowing
teams to learn and adjust as they go. Using Google
Cloud’s managed services, such as Dataproc, D
ataflow,
and Cloud Composer, the transfer of existing workloads
can be simplified. To avoid disruptions when going live,
techniques such as blue-green or canary deployment to
a new system can also help. The unique challenges
involved in migrating large volumes of diverse and
evolving data, given strict privacy and security
regulations, should also be considered by the
automotive industry. Validation checks are a must and
should be implemented at every step of the process to
maintain data quality. Finally, tuning data models and
SQL queries in BigQuery post-migration will help boost
performance and be cost-efficient.
A thorough analysis of the data migration journey,
including Hadoop’s shortcomings and Google BigQuery
as a modern alternative, in the automotive industry has
been demonstrated by this research paper. It also offers
a roadmap with practical steps for companies to
upgrade their data infrastructure. The focus on
achieving zero or near-zero downtime forms an
essential factor to maintain business continuity in a
sector that increasingly depends on real-time data to
drive efficiency, innovation, and competitive advantage.
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