Chase Sapphire Preferred: Maximum rewards for everyday spend


The Chase Sapphire Preferred® Card (see rates and fees) has long been one of our favorite travel cards at TPG. For a $95 annual fee, the Sapphire Preferred delivers outsize value in the form of solid benefits and valuable Chase Ultimate Rewards points.

With its recent refresh, the Sapphire Preferred added more bonus categories. Now, its suite covers nearly every aspect of the typical spender’s everyday expenses.

The card is an excellent option for award travelers of all types, whether you’re a points and miles beginner, a family traveler or a road warrior. Here’s why it works for almost everyone.


Best-ever offer: Earn 100,000 bonus points with the Chase Sapphire Preferred Card. Apply now!


Broad bonus categories on the Sapphire Preferred

There are multiple bonus categories on the Sapphire Preferred, including two new additions. With the card, you’ll earn:

  • 5 points per dollar spent on all Chase Travel℠ purchases*, Lyft rides (through Sept. 30, 2027) and eligible Peloton equipment and accessory purchases over $150 (through Dec. 31, 2027): 10.3% return on spending, per TPG’s June 2026 valuations
  • 3 points per dollar spent on gas† and electric vehicle charging†, vacation homes at eligible brands†‡, dining worldwide (including takeout and eligible delivery services), top streaming services, online grocery purchases (excluding Target, Walmart and wholesale clubs): 6.2% return
  • 2 points per dollar spent on all other travel worldwide: 4.1% return
  • 1 point per dollar spent on all other purchases: 2.1% return

*Purchases include flights, hotels, rental cars, vacation homes, cruises, activities and tours
†New or updated benefit
‡Includes Airbnb, Vrbo, Plum Guide, HomeAway, Homestay.com and Vacasa

People having a backyard picnic at an Airbnb
AIRBNB

With the addition of gas and EV charging and vacation homes booked directly, the Sapphire Preferred has turned into a genuinely competitive everyday spending card — and an excellent candidate for a one-card setup, if simplicity is what you value.

Plus, the Chase Sapphire Reserve® (see rates and fees) notably lost the 3 points per dollar bonus category on general travel in last year’s refresh. So, it’s worth considering that the mid-tier Sapphire Preferred now earns more on eligible vacation rentals than its premium counterpart.

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In the next two parts of this guide, we’ll focus on the everyday spending categories that earn 3 points per dollar spent and on how different types of spenders can make the most of them.

Related: Why the Chase Sapphire Preferred is more than just a starter card

How to maximize earnings with the Sapphire Preferred

The Sapphire Preferred‘s bonus categories cover many everyday purchases.

The addition of gas and EV charging is a major plus with the card’s refresh. Gas can be an overlooked category for many major travel rewards cards, since many of the best cards for gas earn cash back. And with currently high fuel prices in the U.S., there are multiple ways to turn this high cost into valuable rewards. Per TPG’s valuations, you’ll get 6.2% back on every dollar you spend on gas or EV charging.

Electric car charging station
PHONLAMAIPHOTO/GETTY IMAGES

When it comes to food, you’ll continue to earn 3 points per dollar spent on dining worldwide (including takeout and eligible delivery), with no limits. If you prefer to cook, most online grocery purchases will also earn 3 points per dollar spent.

The Sapphire Preferred’s streaming services bonus category stands out as one of the best on the market, as it’s rare for a non-cashback-earning card to offer bonus rewards on streaming services.

And for frequent travelers, the addition of a bonus category for vacation homes is a game changer. It’s an earning category we haven’t seen often on transferable rewards cards.

person in airbnb
AIRBNB

The Sapphire Preferred is rivaled by two other mid-tier cards with solid earning rates on everyday purchases: the Citi Strata Premier® Card (see rates and fees) and the Wells Fargo Autograph Journey℠ Card (see rates and fees). These cards earn Citi ThankYou points and Wells Fargo Rewards points, respectively, which come with different sets of transfer partners.

Consider familiarizing yourself with each of these programs (in addition to Chase Ultimate Rewards) before you make a decision. Overall, TPG’s June 2026 valuations peg Ultimate Rewards points higher than both Citi ThankYou and Wells Fargo Rewards points, but there isn’t a one-size-fits-all answer for all spenders.

Related: 7 Chase Sapphire Preferred benefits you might not know about

Who gets the most out of the Sapphire Preferred?

The Sapphire Preferred could easily work for a variety of spenders. Let’s break down the math with three key groups and how they can maximize their earnings with the card.

Beginners

If you want a simple, one-card setup to get your award travel journey started, the Sapphire Preferred could be a great choice.

Here’s a hypothetical chart of what a single person who’s newer to rewards cards may spend, and how much they would earn in a month and a year with the Sapphire Preferred’s 3-point-per-dollar categories:

Category  Estimated monthly spending Points earned per month  Value per month§  

$200

600

$12

$150

450

$9

$25

75

$2

$60

180

$4

$50

150

$3

$485

1,455 points

$30

§Per TPG’s valuations

With this math, an average single spender could earn 17,460 points annually with these bonus categories alone — worth about $358 per year, or more than three times the card’s $95 annual fee.

BEN SMITHSON/THE POINTS GUY

About 17,000 Ultimate Rewards points could easily get you a one-way domestic United Airlines economy flight. For example, an economy seat from Chicago to San Francisco costs 15,000 points (plus $5.60 in taxes and fees) when transferred to MileagePlus.

Families

Let’s do the same with a family, which will naturally spend more money in certain categories than a single person:

Category  Estimated monthly spending  Points earned per month   Value per month§

$400

1,200

$25

$500

1,500

$31

$50

150

$3

$150

450

$9

$100

300

$6

$1,200

3,600 points

$74

§Per TPG’s valuations

A family with this spending pattern could earn 43,200 points in a year, which would be about $886 in annual value. That’s more than nine times the Sapphire Preferred’s annual fee, just with this set of categories.

The Hyatt Place Fort Lauderdale Cruise Port Pool
HYATT

There’s a lot a family could do with 43,000 Chase Ultimate Rewards points. Transferred to World of Hyatt at the new 4:3 ratio¶, that’s enough for four nights at a Category 3 property, such as the Hyatt House Fort Lauderdale Airport, where award rates start at 8,000 points per night. For families, that could help cover multiple rooms or a few extra nights before or after a cruise.

¶Effective immediately for cardholders who applied on or after June 15; effective Oct. 1 for other cardholders

Frequent travelers

Given that the Sapphire Preferred is a travel card after all, a high-spending frequent traveler who enjoys dining out and booking vacation rentals often will certainly be able to maximize these categories. Here’s a hypothetical:

Category  Estimated monthly spending Points earned per month  Value per month§  

$500

1,500

$31

$150

450

$9

$40

120

$2

$100

300

$6

$400

1,200

$25

$1,190

3,570 points

$73

§Per TPG’s valuations

Frequent travelers with spending patterns similar to this could earn 42,840 points from these bonus categories in a year. With TPG’s valuations placing it at around $878, you’d be getting more than nine times the annual fee back.

Air France Business Class
ERIC ROSEN/THE POINTS GUY

If you top off the roughly 43,000 points you’d earn from these bonus categories with 17,000 more points (plus taxes and fees), you could unlock a business-class seat to Europe on Air France-KLM Flying Blue.

Related: How to maximize your rewards earning with the Chase Sapphire Preferred

Bottom line

The Sapphire Preferred is an excellent choice for a wide range of spenders, in part because it earns 3 points per dollar on the things people buy most. This includes the card’s newly added gas and EV charging and vacation homes categories.

For beginners, families and frequent travelers especially, this could make the Sapphire Preferred the only card you need. Its bonus categories cover a wide range of purchases, giving it high earning potential for a $95 fee that’s easy to justify.

However, the card is not ideal for spenders who prefer straightforward cash-back rewards or people who don’t travel enough to maximize Chase’s transfer partners.

Still, considering how valuable Ultimate Rewards points can be (especially when transferred to airline and hotel partners), the math speaks for itself.

To learn more, read our full review of the Sapphire Preferred.


Apply here: Chase Sapphire Preferred




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The important features of hadoop are:

  • It is an open source programming language code where you can change the code as per your need.
  • Hadoop manages flaws through the replica creation process.
  • In HDFS, Hadoop stores massive amounts of data in a distributed manner. On a cluster of nodes, process the data in parallel.
  • Hadoop is a free and open source platform. As a result, it is an extremely scalable platform. As a result, new nodes can be easily added without causing any downtime.
  • Even after machine failure regarding data replication, information is accurately stored on the cluster of machines. As a result, even if one of the nodes fails, we can still store data reliably.
  • Information is particularly accessible despite hardware failure due to multiple copies of data. As a result, if one machine fails, data can be retrieved from the other path.
  • Hadoop is extremely adaptable when it comes to dealing with various types of data. It handles structured, semi-structured, and unstructured data.
  • There is no need for the client to deal with distributed computing because the framework handles everything. As a result, it is simple to use.

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Hadoop Ecosystem:

Hadoop Ecosystem is a framework or a suite that offers a variety of services to fix complex problems. It includes Apache projects as well as a variety of commercial tools and solutions.Hadoop is composed of four major components: HDFS, MapReduce, YARN, and Hadoop Common. The majority of the techniques or strategies are used to augment or assist these key components. All of these tools work together to provide services such as data absorption, analysis, storage, and maintenance.

Hadoop Ecosystem

Now let us discuss each and every component of the hadoop ecosystem in detail.

HDFS:

Hadoop’s primary storage system is the Hadoop Distributed File System (HDFS). HDFS is a file system that stores very large files on a cluster of commodity hardware. It adheres to the principle of storing fewer large files rather than a large number of small files. HDFS reliably stores data even in the event of hardware failure. As a result, by obtaining in parallel, it offers superior utilization access to the database.

Elements of HDFS:

The two elements of HDFS are namenode and datanode.

  • NameNode – It serves as the master node in a Hadoop cluster. Namenode stores meta-data, such as the number of blocks, replicas, and other information. Meta-data is stored in the master’s memory. The slave node is assigned tasks by NameNode. Because it is the heart of HDFS, it should be deployed on dependable hardware.
  • DataNode – It functions as a slave in a Hadoop cluster. DataNode in Hadoop HDFS is in charge of storing actual data in HDFS. DataNode also performs read and write operations for clients based on their requests. DataNodes can be deployed on commodity hardware as well.

MadReduce:

Hadoop is an acronym for Hadoop Distributed File Hadoop’s data processing layer is MapReduce. It works with large amounts of structured and unstructured data stored in HDFS. MapReduce can also handle massive amounts of data in parallel. It accomplishes this by breaking down the job (submitted job) into a series of independent tasks. MapReduce in Hadoop works by dividing the processing into two phases: Map and Reduce.

  • Map – The first stage of processing in which we define all of the complicated control code.
  • Reduce – This is the second step in the implementation phase of the project. Lightweight processing, such as aggregation/summation, is specified here.

YARN:

The resource management is handled by Hadoop YARN. It is Hadoop’s operating system. As a result, it is in charge of managing and monitoring workloads, as well as implementing security controls. It serves as a centralized platform for delivering data governance tools to Hadoop clusters.

YARN supports a variety of data processing engines, including real-time streaming, batch processing, and so on.

Components of YARN:

The components of YARN are resource and node manager.

The Resource Manager is a cluster-level component that is installed on the Master machine. As a result, it manages resources and schedules applications that run on top of YARN. It is made up of two parts: the Scheduler and the Application Manager.
Node Manager is a component at the node level. It is executed on each slave machine. It communicates with the Resource Manager on a regular basis in order to stay up to date.

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Hive:

The Apache Hive is a free open source data warehouse system that can query and analyze huge databases stored in Hadoop files. In Hadoop, it processes structured and semi-structured data. Hive also supports the analysis of large datasets stored in HDFS and the Amazon S3 filesystem. Hive employs the HiveQL (HQL) language, which is similar to SQL. HiveQL automatically converts SQL queries into mapreduce jobs.

Pig:

It is a high-level language platform designed to run queries on massive datasets stored in Hadoop HDFS. PigLatin is a pig language that is very similar to SQL. Pig loads the data, applies the necessary filters, and dumps the data in the appropriate format. Pig also converts all operations into Map and Reduce tasks that are efficiently processed by Hadoop.

Components of pig:

The components of pig are: extensible, self optimizing and handles all kinds of data.

  • Extensible  Pig users can write custom functions to meet their specific processing needs.
  • Self-optimization allows the system to optimize itself. As a result, the user can concentrate on semantics.
  • Handles all types of data i.e both structured and unstructured data.

HBase:

Apache HBase is a NoSQL database that runs on Hadoop. It’s a database that holds structured data in tables with billions of rows and millions of columns. HBase also allows you to read or write data in HDFS in real time.

Components of HBase:

HBase Master – This is not a data storage system. However, it is in charge of administration (interface for creating, updating and deleting tables.).
The Region Server is the worker node. It handles client read, write, update, and delete requests. The region server process is also executed on each node in the Hadoop cluster.

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HCatalog:

On top of Apache Hadoop, it is a table and storage management layer. Hive relies heavily on HCatalog. As a result, it allows the user to save their data in any format and structure. It also allows different Hadoop components to read and write data from the cluster with ease.

Advantages of HCatalog:

  • Make data cleaning and archiving tools visible.
  • HCatalog’s table abstraction frees the user from the overhead of data storage.
  • Allows data availability notifications.

Arvo:

It is an open source project that provides Hadoop with data serialization and data exchange services. Service programs can serialize data into files or messages by using serialization. It also stores both the data definition and the data in a single message or file. As a result, programs can easily understand information stored in an Avro file or message on the fly.

Arvo provides the following.

  • Persistent data is stored in a container file.
  • Call for a remote procedure.
  • Data structures that are rich.
  • Binary data format that is small and fast.

Thrift:

Apache Thrift is a software framework that enables the development of scalable cross-language services. Thrift is also used to communicate with RPCs. Because Apache Hadoop makes a lot of RPC calls, there is a chance that Thrift can help with performance.

Drill:

The drill is used to process large amounts of data on a large scale. The drill is designed to scale to thousands of nodes and query petabytes of data. It is also a distributed query engine with low latency for large-scale datasets. In addition, the drill is the first distributed SQL query engine with a schema-free model.

The characteristics of drill are:

  • Drill decentralized metadata – Drill does not necessitate centrally controlled metadata. Drill users do not need to create or manage metadata tables in order to query data.
  • Drill provides a hierarchical columnar data model for flexibility. It is capable of representing complex, highly dynamic data while also allowing for efficient processing.
  • To begin the query execution process, use dynamic schema discovery. Drill does not require any data type specifications. Drill instead begins processing the data in units known as record batches. During processing, it also discovers schema on the fly.

Mahout:

It is a free and open source framework for developing scalable machine learning algorithms. Mahout provides data science tools to automatically find meaningful patterns in Big Data sets after we store them in HDFS.

Sqoop:

It is primarily used for data import and export. As a result, it imports data from external sources into Hadoop components such as HDFS, HBase, and Hive. It also exports Hadoop data to other external sources. Sqoop is compatible with relational databases like Teradata, Netezza, Oracle, and MySQL.

Flume:

Flume efficiently collects, aggregates, and moves a large amount of data from its origin to HDFS. It has a straightforward and adaptable architecture based on streaming data flows. Flume is a fault-tolerant and dependable mechanism. Flume also allows data to be flowed from a source into a Hadoop environment. It employs a simple extensible data model that enables online analytic applications. As a result, we can use Flume to immediately load data from multiple servers into Hadoop.

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Ambari:

It is a management platform that is open source. It is a platform for setting up, managing, monitoring, and securing an Apache Hadoop cluster. Ambari provides a consistent, secure platform for operational control, making Hadoop management easier.

Advantages of ambari are:

  • Simplified installation, configuration, and management – It can create and manage large-scale clusters quickly and easily.
  • Ambari configures cluster security across the entire platform using a centralized security setup. It also reduces the administration’s complexity.
  • Ambari is fully configurable and extensible for bringing custom services under management.
  • Full visibility into cluster health – Using a holistic approach to monitoring, Ambari ensures that the cluster is healthy and available.

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ZooKeeper:

Zookeeper is a centralized service in Hadoop. It stores configuration information, handles naming, and offers distributed synchronization. It also has group services. Zookeeper is also in charge of managing and coordinating a large group of machines.

The benefits of zookeeper are:

  • Fast – Zookeeper performs well in workloads where reads to data outnumber writes. The ideal read/write ratio is ten to one.
  • Ordered – Zookeeper keeps a record of all transactions, which can be used for high-level reporting.

Oozie:

It is a system for managing Apache Hadoop jobs via a workflow scheduler. It sequentially combines multiple jobs into a single logical unit of work. As a result, the Oozie framework is fully integrated with the Apache Hadoop stack, with YARN serving as the architecture center. It also supports Apache MapReduce, Pig, Hive, and Sqoop jobs.

Oozie is both scalable and adaptable. Jobs can be easily started, stopped, suspended, and rerun. As a result, Oozie makes it very simple to rerun failed workflows. It is also possible to bypass a particular failed node.

There are two kinds of Oozie jobs:

  • Oozie workflow is used to process and run workflows made up of Hadoop jobs such as MapReduce, Pig, and Hive.
  • Oozie coordinator schedules and executes workflow jobs based on predefined schedules and data availability.

Conclusion:

Hadoop Ecosystem supports multiple components that contribute to its prominence. Several Hadoop job roles also are available as a result of these Hadoop components. I hope you found this Hadoop Ecosystem tutorial useful in comprehending the Hadoop family and their responsibilities. If you have any questions, please just leave them in the comment stream.

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