Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 1 of 21 PageID #: 1
`
`IN THE UNITED STATES DISTRICT COURT
`FOR THE EASTERN DISTRICT OF TEXAS
`SHERMAN DIVISION
`
`
`
`
`
`
`
`
`
`
`Civil Action No. 4:23-cv-01147
`
`Jury Trial Demanded
`
`
`R2 Solutions LLC,
`
` Plaintiff,
`
`v.
`
`Databricks, Inc.,
`
` Defendant.
`
`
`COMPLAINT FOR PATENT INFRINGEMENT
`
`Plaintiff R2 Solutions LLC files this Complaint against Databricks, Inc. for infringement
`
`
`
`
`
`
`
`
`of U.S. Patent No. 8,190,610 (“the ’610 patent”). The ’610 patent is sometimes referred to as the
`
`“patent-in-suit.”
`
`THE PARTIES
`
`1.
`
`Plaintiff R2 Solutions LLC (“R2”) is a Texas limited liability company located in
`
`Frisco, Texas.
`
`2.
`
`Defendant Databricks, Inc. (“Databricks”) is a Delaware corporation
`
`headquartered at 160 Spear St., Suite 1300, San Francisco, CA 94105 and has a regular and
`
`established place of business in this District at 6900 Dallas Pkwy, Suite 02-106, Plano, TX
`
`75024. Databricks may be served with process through its registered agent, United Agent Group
`
`Inc., at 5444 Westheimer, #1000, Houston, TX 77056.
`
`JURISDICTION AND VENUE
`
`3.
`
`This action arises under the patent laws of the United States, 35 U.S.C. § 101, et
`
`seq. This Court’s jurisdiction over this action is proper under the above statutes, including 35
`
`

`

`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 2 of 21 PageID #: 2
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`U.S.C. § 271, et seq., 28 U.S.C. § 1331 (federal question jurisdiction), and 28 U.S.C. § 1338
`
`(jurisdiction over patent actions).
`
`4.
`
`This Court has personal jurisdiction over Databricks because, among other things,
`
`Databricks does business in this State by, among other things, “recruit[ing] Texas residents,
`
`directly or through an intermediary located in this State, for employment inside or outside this
`
`State.” Tex. Civ. Prac. & Rem. Code § 17.042(3). For instance, Databricks has multiple job
`
`openings in Texas as of December 18, 2023:1
`
`
`
`
`
`1 https://www.databricks.com/company/careers/open-positions?department=all&location=Texas;
`https://www.linkedin.com/jobs/search/?currentJobId=3782993305&f_C=3477522&geoId=102748797&keywords=d
`atabricks&location=Texas%2C%20United%20States&origin=JOB_SEARCH_PAGE_SEARCH_BUTTON&refres
`h=true; https://www.linkedin.com/jobs/search/?currentJobId=3765311929&f_C=3477522&geoId=92000000&
`keywords=texas&origin=COMPANY_PAGE_JOBS_KEYWORD.
`2
`
`
`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 3 of 21 PageID #: 3
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`

`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 4 of 21 PageID #: 4
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`5.
`
`And according to its LinkedIn page, Databricks has 278 employees in its Texas
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`office (as of December 18, 2023):2
`
`
`
`6.
`
`Further, this Court has personal jurisdiction over Databricks because it has
`
`engaged, and continues to engage, in continuous, systematic, and substantial activities within this
`
`State, including the substantial marketing and sale of products and services within this State and
`
`this District. Indeed, this Court has personal jurisdiction over Databricks because it has
`
`committed acts giving rise to R2’s claims for patent infringement within and directed to this
`
`
`
`2 https://www.linkedin.com/company/databricks/people/?facetGeoRegion=102748797
`4
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 5 of 21 PageID #: 5
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`District, has derived substantial revenue from its goods and services provided to individuals and
`
`entities in this State and this District, and maintains regular and established places of business in
`
`this District, including at least its brick-and-mortar location in Plano, Texas.3
`
`
`
`7.
`
`Relative to patent infringement, Databricks has committed and continues to
`
`commit acts in violation of 35 U.S.C. § 271, and has made, used, offered for sale, and/or sold
`
`infringing products, systems, and/or services in this State, including this District, and has
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`otherwise engaged in infringing conduct within and directed at, or from, this District. Such
`
`infringing products, systems, and/or services (collectively, the “Accused Instrumentalities”)
`
`include the Databricks Data Intelligence Platform/Databricks Lakehouse Platform, and any other
`
`platform(s) offered or provided by Databricks that utilize Apache Spark or any other similar
`
`functionality.
`
`
`
`3https://www.databricks.com/company/contact/office-locations.
`5
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 6 of 21 PageID #: 6
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`8.
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`Databricks’ infringing activities have caused harm to R2 in this District.
`
`Databricks and/or its partners offer to sell and sell the Accused Instrumentalities within this
`
`District, and on information and belief, Databricks, its partners, and/or their customers make the
`
`Accused Instrumentalities in this District and use the Accused Instrumentalities in this District in
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`an infringing manner. For example, Databricks, its partners, and/or their customers (induced by
`
`Databricks) implement and exert control over the Accused Instrumentalities via cloud-based and
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`on-premises solutions that utilize computers and/or servers located in this District. Outputs from
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`such methods and systems are generated by and/or delivered to devices implementing the
`
`Accused Instrumentalities in this District. Databricks and/or its partners provide the Accused
`
`Instrumentalities (and services therewith) to customers in this District, and Databricks’
`
`customers in this District obtain data analytics facilitated by the Accused Instrumentalities,
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`whether via Databricks’ implementation of the Accused Instrumentalities on their behalf, or via
`
`their use of the Accused Instrumentalities provided to them by Databricks. These are purposeful
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`acts and transactions in this State and this District such that Databricks reasonably should know
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`and expect that it could be haled into this Court.
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`9.
`
`Venue is proper in this District under 28 U.S.C. §§ 1391 and 1400(b) because
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`Databricks has a regular and established place of business in Plano, which is in this District.
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`Venue is further proper in this District because Databricks has directly infringed and/or induced
`
`the infringement of others, including its customers, in this District. As set out above, Databricks
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`has at least offered for sale and sold the Accused Instrumentalities in this District and has used
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`the Accused Instrumentalities in an infringing manner in this District. In addition, Databricks’
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`customers have made and continue to make the Accused Instrumentalities in this District, and
`
`have used and continue to use the Accused Instrumentalities in an infringing manner in this
`
`6
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 7 of 21 PageID #: 7
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`District. These infringements were, and continue to be, induced by Databricks (as set out further
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`below).
`
`BACKGROUND
`
`10.
`
`The patent-in-suit was filed by Yahoo! Inc. (“Yahoo!”) in 2006. At the time,
`
`Yahoo! was a leading Internet communications, commerce, and media company. Yahoo!
`
`invested billions of dollars in research and development over this period, filing hundreds of
`
`patent applications each year to cover the innovative computing technologies emerging from its
`
`expansive research and development efforts.
`
`11.
`
`Yahoo! began as a directory of websites that two Stanford graduate students
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`developed as a hobby. The name “Yahoo” stands for “Yet Another Hierarchical Officious
`
`Oracle,” a nod to how the original Yahoo! database was arranged hierarchically in layers of
`
`subcategories. From this initial database, Yahoo! would develop and promulgate numerous
`
`advancements in the field of data storage and recall.
`
`12.
`
`For example, in 1995, Yahoo! introduced Yahoo! Search. This software allowed
`
`users to search the Yahoo! directory, making it the first popular online directory search engine.
`
`This positioned Yahoo! as the launching point for most users of the World Wide Web. By 1998,
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`Yahoo! had the largest audience of any website or online service. In the early 2000s, Yahoo!
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`continued to develop its suite of technologies in the web search and database industries. The
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`patent-in-suit relates to innovations during this period associated with data analytics.
`
`THE PATENT-IN-SUIT
`
`13.
`
`The ’610 patent is entitled, “MapReduce for Distributed Database Processing.”
`
`The ’610 patent lawfully issued on May 29, 2012, and stems from U.S. Patent Application No.
`
`7
`
`
`

`

`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 8 of 21 PageID #: 8
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`11/539,090, which was filed on October 5, 2006. A copy of the ’610 patent is attached hereto as
`
`Ex. 1.
`
`14.
`
`R2 Solutions is the owner of the patent-in-suit with all substantial rights,
`
`including the exclusive right to enforce, sue, and recover damages for past and future
`
`infringements.
`
`15.
`
`The claims of the patent-in-suit are directed to patent-eligible subject matter under
`
`35 U.S.C. § 101. They are not directed to abstract ideas, and the technologies covered by the
`
`claims consist of ordered combinations of features and functions that, at the time of invention,
`
`were not, alone or in combination, well-understood, routine, or conventional.
`
`16.
`
`Indeed, the specification of the patent-in-suit discloses shortcomings in the prior
`
`art and then explains, in detail, the technical way the claimed inventions resolve or overcome
`
`those shortcomings. For example, the specification explains that “conventional MapReduce
`
`implementations do not have facility to efficiently process data from heterogeneous sources” and
`
`that “it is impractical to perform joins over two relational tables that have different schemas.”
`
`’610 patent at 3:9-20. To solve these problems, the ’610 patent provides a clear technological
`
`improvement to existing MapReduce systems by describing and implementing a novel
`
`MapReduce architecture where mapping and reducing functions can be applied to data from
`
`heterogeneous data sources (i.e., data sources having different schema) to accomplish the merger
`
`of heterogeneous data based on a key in common between or among the heterogeneous data. For
`
`example, the ’610 patent explains how implementation of, e.g., “data groups” realizes these
`
`improvements:
`
`In general, partitioning the data sets into data groups enables a mechanism to
`associate (group) identifiers with data sets, map functions and iterators (useable
`within reduce functions to access intermediate data) and, also, to produce output
`
`8
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`

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`data sets with (group) identifiers. It is noted that the output group identifiers may
`differ from the input/intermediate group identifiers.
`
`’610 patent at 3:58-64.
`
`17.
`
`The technological advantages of a “data group”-centric system are shown to
`
`“enhance[] the utility of the MapReduce programming methodology.” ’610 patent at 1:32-33. As
`
`the specification explains:
`
`[T]he MapReduce concept may be utilized to carry out map processing
`independently on two or more related datasets (e.g., related by being characterized
`by a common key) even when the related data sets are heterogeneous with respect
`to each other, such as data tables organized according to different schema. The
`intermediate results of the map processing (key/value pairs) for a particular key
`can be processed together in a single reduce function by applying a different
`iterator to intermediate values for each group. In this way, operations on the two
`or more related datasets may be carried out more efficiently or in a way not even
`possible with the conventional MapReduce architecture.
`
`Id. at 8:47-58.
`
`18.
`
`Such a solution is embodied, for example, in Claim 1 of the ’610 patent:
`
`A method of processing data of a data set over a distributed system, wherein the
`data set comprises a plurality of data groups, the method comprising:
`partitioning the data of each one of the data groups into a plurality of data
`partitions that each have a plurality of key-value pairs and providing each
`data partition to a selected one of a plurality of mapping functions that are
`each user-configurable to independently output a plurality of lists of values for
`each of a set of keys found in such map function’s corresponding data
`partition to form corresponding intermediate data for that data group and
`identifiable to that data group, wherein the data of a first data group has a
`different schema than the data of a second data group and the data of the
`first data group is mapped differently than the data of the second data group
`so that different lists of values are output for the corresponding different
`9
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`

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`intermediate data, wherein the different schema and corresponding different
`intermediate data have a key in common; and
`reducing the intermediate data for the data groups to at least one output data
`group, including processing the intermediate data for each data group in a
`manner that is defined to correspond to that data group, so as to result in a
`merging of the corresponding different intermediate data based on the key
`in common,
`wherein the mapping and reducing operations are performed by a distributed
`system.
`
`(emphasis added).
`
`19.
`
`The concept of “data groups” as found in Claim 1 of the ’610 patent in the context
`
`of MapReduce attains a novel and technological improvement in computer capabilities. For
`
`example, employing “data groups” allows a diverse data set to be fed to collections of mapping
`
`and reducing functions within the same MapReduce architecture to ultimately be joined and/or
`
`merged in spite of the diversity. Per Claim 1, the improved MapReduce architecture in the
`
`reducing phase is able to selectively employ specialized processing based on the “data group”
`
`from which the data being reduced originated, and this specialized processing enables the
`
`MapReduce architecture in the reducing phase to accomplish the merger of intermediate data
`
`hailing from different data groups.
`
`20.
`
`The inventions described and claimed in the ’610 patent improve the speed,
`
`efficiency, effectiveness, and functionality of computer systems. Moreover, the inventions
`
`provide an improvement in computer functionality rather than improvement in performance of an
`
`economic task or other tasks for which a computer is used merely as a tool. The ’610 patent itself
`
`states that the claimed inventions “enhance[] the utility of the MapReduce programming
`
`methodology.” ’610 patent at Abstract, 1:31-33, 1:66-2:2. The ’610 patent specification goes on
`
`to explain that “[t]he intermediate results of the map processing (key/value pairs) for a particular
`10
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 11 of 21 PageID #: 11
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`key can be processed together in a single reduce function by applying a different iterator to
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`intermediate values for each group.” Id. at Abstract, 1:37-39, 2:4-8. And the specification
`
`discusses the use of multiple processors to perform processing functions in parallel. See id. As a
`
`result, computer functionality is improved. Id. at 1:42-44.
`
`21.
`
`Additionally, the claimed inventions provide for more dynamic, customizable,
`
`and efficient processing of large sets of data. See, e.g., ’610 patent at 2:58-61, 4:18-22. The
`
`inventions provide optimization of such processing, which increases efficiency and reduces
`
`processor execution time. For example, the specification describes a combiner function that
`
`“helps reduce the network traffic and speed up the total execution time.” ’610 patent at 3:1-8.
`
`The specification also discusses the use of configurable settings to reduce processing overhead.
`
`See, e.g., id. at 4:60-62, 5:33-39.
`
`22.
`
`In essence, the patent-in-suit relates to novel and non-obvious inventions in the
`
`fields of data analytics and database structures.
`
`DEFENDANT’S PRE-SUIT KNOWLEDGE OF ITS INFRINGEMENT
`
`23.
`
`Prior to the filing of this Complaint, Databricks was notified on numerous
`
`occasions of the ’610 patent and the R2 portfolio to which the ’610 patent belongs.
`
`24.
`
`On April 28, 2022, R2 filed suit against American Airlines, Inc., styled R2
`
`Solutions LLC v. American Airlines, Inc., Case No. 4:22-cv-00353 (E.D. Tex. Apr. 28, 2022) (the
`
`“AA litigation”), alleging infringement of the ’610 patent.
`
`25.
`
`On January 10, 2023, R2 served Databricks with a subpoena in connection with
`
`the AA litigation. The subpoena specifically identified the ’610 patent and sought materials and
`
`testimony regarding Databricks’ systems and products that are now accused in this lawsuit.
`
`11
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 12 of 21 PageID #: 12
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`26.
`
`On information and belief, Databricks has had knowledge of the ’610 patent and
`
`its infringements since shortly after April 28, 2022, when R2 filed the AA litigation. At the very
`
`least, Databricks has had knowledge of the ’610 patent since being served with a subpoena in
`
`connection with the AA litigation on January 10, 2023.
`
`COUNT I
`INFRINGEMENT OF U.S. PATENT NO. 8,190,610
`
`This cause of action arises under the patent laws of the United States, and in
`
`27.
`
`particular, 35 U.S.C. §§ 271, et seq.
`
`28.
`
`R2 Solutions is the owner of the ’610 patent with all substantial rights to the ’610
`
`patent, including the exclusive right to enforce, sue, and recover damages for past and future
`
`infringements.
`
`29.
`
`The ’610 patent is valid and enforceable and was duly issued in full compliance
`
`with Title 35 of the United States Code.
`
`Direct Infringement (35 U.S.C. § 271(a))
`
`30.
`
`Databricks has directly infringed, and continues to directly infringe, one or more
`
`claims of the ’610 patent in this District and elsewhere in Texas and the United States.
`
`31.
`
`To this end, Databricks has infringed and continues to infringe, either by itself or
`
`via an agent, at least claims 1-32 of the ’610 patent by, among other things, making, offering to
`
`sell, selling, and/or using the Accused Instrumentalities.
`
`32.
`
`For example, Databricks uses the Accused Instrumentalities in an infringing
`
`manner as detailed in Exhibit 2. Databricks both uses the Accused Instrumentalities for itself and
`
`implements the Accused Instrumentalities to provide analytics services to its customers.
`
`Databricks offers these services on a per-“Databricks Unit” (“DBU”) basis, and a “DBU” “is a
`
`normalized unit of processing power on the Databricks Lakehouse Platform used for
`
`12
`
`
`

`

`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 13 of 21 PageID #: 13
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`measurement and pricing purposes. The number of DBUs a workload consumes is driven by
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`processing metrics, which may include the compute resources used and the amount of data
`
`processed.”4
`
`33.
`
`In addition, on information and belief, Databricks makes and uses the Accused
`
`Instrumentalities for itself and for its customers. Databricks also offers to sell, and sells, the
`
`Accused Instrumentalities to its customers for implementation directly by the customers. Such
`
`making, offering to sell, and selling directly infringes the ’610 patent as detailed in Exhibit 3.
`
`34.
`
`Databricks is liable for its direct infringements of the ’610 patent pursuant to 35
`
`U.S.C. § 271.
`
`Indirect Infringement (Inducement – 35 U.S.C. § 271(b))
`
`35.
`
`In addition and/or in the alternative to its direct infringements, Databricks has
`
`indirectly infringed and continues to indirectly infringe one or more claims of the ’610 patent by
`
`inducing direct infringement by its customers, partners, and end users.
`
`36.
`
`On information and belief, Databricks has had knowledge of the ’610 patent and
`
`its infringements since shortly after April 28, 2022, when R2 filed the AA litigation. At the very
`
`least, Databricks has had knowledge of the ’610 patent and its infringements since being served
`
`with a subpoena in connection with the AA litigation on January 10, 2023.
`
`37.
`
`Despite having knowledge of the ’610 patent and knowledge of its scope,
`
`Databricks has specifically intended, and continues to specifically intend, for persons (such as
`
`Databricks’ customers, partners, and end users) to make the Accused Instrumentalities and use
`
`the Accused Instrumentalities in ways that infringe the ’610 patent, including at least claims 1-
`
`4 https://www.databricks.com/product/pricing.
`
`
`
`
`13
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 14 of 21 PageID #: 14
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`32. Databricks has also specifically intended, and continues to specifically intend, for its partners
`
`to offer for sale and sell the Accused Instrumentalities. Databricks knew or should have known
`
`that its actions have induced, and continue to induce, such infringements.
`
`38.
`
`Databricks provides the Accused Instrumentalities to its customers:5
`
`39.
`
`Databricks also provides its partners with the Accused Instrumentalities for
`
`distribution, resale, and/or to enable its partners to provide data analytics services to end users:
`
`
`
`5 https://www.databricks.com/customers.
`
`
`
`
`14
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`

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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 15 of 21 PageID #: 15
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`
`
`40.
`
`Databricks instructs and encourages partners, customers, and end users to make
`
`the Accused Instrumentalities and use the Accused Instrumentalities in ways that infringe the
`
`’610 patent. For example, the Databricks’ website includes a “Documents” page with explicit
`
`instructions on how to implement and operate each Accused Instrumentality in an infringing
`
`manner:6
`
`
`
`
`6https://docs.databricks.com/en/;
`https://docs.databricks.com/en/spark/index.html;
`https://docs.databricks.com/en/getting-started/dataframes-python.html;
`https://docs.databricks.com/en/getting-started/dataframes-r.html;
`https://docs.databricks.com/en/getting-started/dataframes-scala.html;
`https://docs.databricks.com/en/delta-live-tables/transform.html.
`
`
`15
`
`
`

`

`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 16 of 21 PageID #: 16
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`
`
`mentation SUPPORT—FEEDBACICOMMUNITY TRY DATABRICKS
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`&databricks HelpCenter
`Dog
`Knowledge Base
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`Search Documentation
`English
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`Databricks on AWS
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`Get started
`
`Whatis Databricks?
`
`Release notes
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`Connectto data sources
`
`Discover data
`
`Query data
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`Load data
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`Prepare data
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`Monitor data and Al assets
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`Share data (Delta sharing)
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`Generative Al & LLMs
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`Machine learning
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`Data governance
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`Lakehousearchitecture
`Reference
`=
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`Documentation > Databricks documentation
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`Databricks documentation
`
`Databricks documentation provides how-to guidance and reference information for data analysts, data scientists,
`and data engineerssolving problemsin analytics and Al. The Databricks DataIntelligence Platform enables data
`teamsto collaborate on datastored in the lakehouse. See Whatis a data lakehouse?.
`
`In
`
`this ar
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`
`
`Try Detaricks
`Whatdo you want to do?
`Manage Databricks
`Reference Guides
`Resources
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`Try Databricks
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`* Query data froma notebook
`* Build a basic ETLpipeline
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` Documentation > Databricks data engineering > Apache Spark on Databricks
`
`
`Getstarted
`In this article:
`Whatis Databricks?
`Whatis therelationshipof ApacheSparktoDatabricks?
`A pac he S pa rk on Data b riCc ks
`Release notes
`How does ApacheSpark work on Databrricks?
`Can| use Databricks without using Apache Spark?
`Connectto data sources
`Why use ApacheSpark on Databricks?
`This article describes how ApacheSparkis related to Databricks and the Databricks DataIntelligence Platform.
`
`
`Discover data
`
`
`Apache Sparkis at the heart of the Databricks platform andis the technology powering computeclusters and SQL
`Query data
`warehouses. Databricksis an optimizedplatform for Apache Spark, providing anefficient and simple platform for
`Load data
`
`
`running Apache Spark workloads.
`Prepare data
`
` Monitor data and Al assets
`Share data (Delta sharing)
`Whatis the relationship of Apache Spark to
`
`
`Databricks Marketplace
`Databricks?
`Data engineering
`The Databricks company was founded bytheoriginal creators of Apache Spark. As an open source software
`
`Delta Live Tables
`project, Apache Spark has committers from many top companies, including Databricks.
`
` Structured Streaming Databricks continues to develop and release features to ApacheSpark. The Databricks Runtime includes additional
`
`
`Apache Spark
`optimizations and proprietary features that build on and extend ApacheSpark,including Photon, an optimized
`
`
`Tutorial: Load and transform
`version of Apache Spark rewritten in C++.
`
`
`data in PySpark DataFrames
`
`Tutorial: Work with SparkR
`Databricks
`
`SperkDataFrames on
`How does Apache Spark work on Databricks?
`
`
`Tutorial Work with Apache
`Whenyou deploy a computecluster or SQL warehouse on Databrieks, Apache Sparkis configured and deployed to
`Spark Scala DataFrames
`
`virtual machines. You don’t needto configureorinitialize a Spark context or Spark session, as these are managedfor
`
`
`Compute
`you by Databricks.
`
` Notebooks
`
`Workflows
`
`
`Libraries
`Can | use Databricks without using Apache Spark?
`
`
`
`
`
`16
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`€
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`Case 4:23-cv-01147-ALM Document 1 Filed 12/28/23 Page 17 of 21 PageID #: 17
`Page 17 of 21 PagelD #: 17
`Case 4:23-cv-01147-ALM Document 1
`Filed
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` databricks
`
`>
`Tutorial: Load and transformdatain PySpark
`
` Frames
`
`
`
`Tutorial: Load and transform data in
`PySpark DataFrames
`This article shows you howto load andtransform U.S. city data using the Apache Spark Python (PySpark)
`DataFrame API in Databricks.
`By the endofthis article, youwill understand what a DataFrame is and feel comfortable with the following tasks.
`
`See also
`
`
`COMMUNITY—SUPPORT BLAUlelecy
` Help Center
`Knowledge Base
`
`
`
`cumentation
`
`English
`aws Amazon WebServices
`
`
`Databricks on AWS
`Getstarted
`Whatis Databricks?
`Release notes
`Connectto data sources
`Discover data
`Query data
`Loaddata
`Prepare data
`Monitor data and Al assets
`Share data (Delta sharing)
`Databricks Marketplace
`Data engineering
`Delta Live Tables
`Structured Streaming
`Apache Spark
`Tutorial: Load and transform
`data in PySpark DataFrames
`Tutorial: Work with SparkR
`SparkDataFrames on
`Databricks
`Tutorial: Work with Apache
`Spark Scala DataFrames
`Compute
`Notebooks
`Workflows
`Libraries
`
`What is a DataFrame?
`A DataFrameis @ two-dimensionallabeled data structure with columns ofpotentially different types. You can think
`of a DataFramelike a spreadsheet, a SQLtable, or a dictionary of series objects. Apache Spark DataFramesprovide
`arich set offunctions (select columns, filter, join, aggregate) that allow you to solve commondata analysis problems
`efficiently.
`Apache Spark DataFrames are an abstraction built on top of Resilient Distributed Datasets (RDDs). Spark
`DataFrames and Spark SQL use a unifiedplanning and optimization engine, allow ving you to get nearly identical
`performance acrossall supportedlanguages on Databricks (Python, SQL, Scala, and R)
`
`toa
`
`>
`
`
`
`
`
`COMMUNITY—SUPPORT TRY DATABRICKS
`Help Center
`Knowledge Base
`databricks
`
`
`
`
`Amazon WebServices
` mentation
`
`
` Databricks on AWS
`
`
`
` Tutorial: V
`Getstarted
`rk with SparkR SparkDataFrameson Databricks
`
`
`Whatis Databricks?
`
`Release notes
`Tutorial: Work with SparkR
`Connect to data sources
`Discover data
`SparkDataFrames on Databricks
`
`Query data
`Load data
`
`This article show
`you howto load andtransform data using the SparkDataFrame API for SparkR in Databricks.
`Prepare data
`You can practice running each ofthis article's code examples from a cell within an R
`that is
`
`Monitor data and Al assets
`
`running
`Databricks clusters provide the
`packagepreinstalled, so that you canstart
`
`
`Share data (Delta sharing)
`working with the
`API right away.
`
`
`
`Databricks Marketplace
`
`
`Data engineering
`Whatis a SparkDataFrame?
`Delta Live Tables
`It is conceptually equivalent to
`A SparkDataFrameis a distributed collection of data organizedinto named columns.
`Structured Streaming
`a table in a database or a data framein R. SparkDataFrames can be constructedfroma widearray of sources such
`
`‘ApacheSpark
`as structureddatafiles, tables in databases, or existing local R data frames. SparkDataFramesprovide a
`rich set of
`Tutorial: Load and transfo