[{"data":1,"prerenderedAt":39},["ShallowReactive",2],{"article":3},{"id":4,"category":5,"slug":6,"title":7,"image":8,"page_image":9,"published_at":10,"updated_at":10,"meta_title":11,"meta_description":12,"meta_keywords":13,"content":14,"translations":15,"tags":36,"faqs":38},143,"blog","selecting-databases-for-large-datasets","Selecting databases for large datasets","https://blog.dexodata.com/storage/uploads/previews/23-7-s-trusted-proxy-website-selecting-databases-for-large-datasets-cover-240b7463-3334-4f9e-ae06-c19732100d1c.webp","https://blog.dexodata.com/storage/uploads/covers/23-7-b-trusted-proxy-website-selecting-databases-for-large-datasets-cover-38ee9edb-0310-4cdc-a865-0465c2e45daf.webp","2025/03/06","How to choose a database for data scraped via proxies","What to consider while choosing a database to work with significant amounts of data harvested via geo targeted proxies provided by the Dexodata proxy site.","buy residential and mobile proxies","\u003C!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.0 Transitional//EN\" \"http://www.w3.org/TR/REC-html40/loose.dtd\">\n\u003C?xml encoding=\"utf-8\"?>\u003Chtml>\u003Cbody>\u003Cp>\u003Cem>\u003Cstrong>Contents of article:\u003C/strong>\u003C/em>\u003C/p>\r\n\u003Cul>\r\n\u003Cli>\u003Ca href=\"#anchor1\">What is a database?\u003C/a>\u003C/li>\r\n\u003Cli>\u003Ca href=\"#anchor2\">NoSQL sub-classes\u003C/a>\u003C/li>\r\n\u003Cli>\u003Ca href=\"#anchor3\">SQL's ACID characteristics\u003C/a>\u003C/li>\r\n\u003Cli>\u003Ca href=\"#anchor4\">Confronting SQL against NoSQL\u003C/a>\u003C/li>\r\n\u003Cli>\u003Ca href=\"#anchor5\">Databases' pluses and shortcomings summarized. SQL vs NoSQL\u003C/a>\u003C/li>\r\n\u003C/ul>\r\n\u003Cp>Modern businesses tackle ever-growing data volumes, ripe for harvesting, processing, storing. Back \u003Ca href=\"https://online.usi.edu/degrees/business/mba/data-analytics/big-data-big-business/\" target=\"_blank\" rel=\"noopener\">in 2023, 3.5 quintillion bytes of data were generated daily\u003C/a>. Numbers of bytes will continue surging. To keep up with the pace, teams in all industries apply advanced, automated, smart data harvesting solutions.  \u003C/p>\r\n\u003Cp>In line with this, web scraping is an important reason why customers contact Dexodata to buy residential and mobile proxies, as well as datacenter IPs. Their objective is grabbing heterogeneous content at enormous scales. Our mission is to make such results feasible. In the capacity of a leading global data harvesting enabler, we master this trade. Yet, this piece discusses no information gathering techniques. It elucidates on what to do next, i.e. what databases should one opt for with giant datasets at stake.\u003C/p>\r\n\u003Ch2>\u003Ca name=\"anchor1\">\u003C/a>What is a database?\u003C/h2>\r\n\u003Cp>Outlining fitting database options for data harvesting initiatives could be a challenging endeavor. Such decisions entail enduring financial, technological, workflow-specific commitments. Investing lots of funds into correct \u003Ca href=\"https://dexodata.com/en/blog/what-are-the-benefits-of-ai-based-models-for-data-extraction\" target=\"_blank\" rel=\"noopener\">AI-driven information collection tools\u003C/a> and proper geo targeted proxies, coupled with mismatching databases, is a direct road towards disappointments, unnecessary spending, etc. Discovering that one has picked up wrong databases leads to undertaking risky and expensive migration and rearrangement processes. In case you intend not only to work with data, but apply it to, say, craft software solutions, consequences can be even worse: rebuilding an app may be even more tiresome and resource-intensive than engineering it from scratch.\u003C/p>\r\n\u003Cp>Before zooming in on the topic, let's establish some key terminology issues of importance. Crucial notions in existence here cover the dilemma of \"non-relational\" viewpoints confronted by \"relational\" perspectives:\u003C/p>\r\n\u003Col>\r\n\u003Cli>\u003Cstrong>Relational choices\u003C/strong>, contrary to non-relational alternatives, structure data via good old tables, comprising habitual time-tried rows and columns. Such tables establish relationships, ensuring that data entities all feature well-defined locations. Pros of employing relational techniques pertain immediately to provision of straightforward and clear-cut frameworks. Databases are queried by means of \u003Ca href=\"https://www.geeksforgeeks.org/structured-query-language/\" target=\"_blank\" rel=\"noopener\">“Structured Query Language (SQL)”\u003C/a>, which is why they are popularly known as SQL databases.\u003C/li>\r\n\u003Cli>\u003Cstrong>Non-relational\u003C/strong> ones, frequently described as NoSQL (aka “Not Only SQL”) options, represent a specific class of database management approaches. What differentiates them is their departure from traditional relational models applied to data organization. They exhibit elastic schemas and are specifically intended to handle substantial volumes of organized and unorganized data.\u003C/li>\r\n\u003C/ol>\r\n\u003Cp>NB: Take notice of a source of potential confusion. While \u003Ca href=\"https://www.cs.miami.edu/home/burt/learning/Csc598.073/notes/reldb.html\" target=\"_blank\" rel=\"noopener\">relational databases\u003C/a> are frequently associated with SQL databases, those are distinct phenomena. SQL serves as a coding language, tailored-fit for relational database management, offering a commonly-shared method for data interaction. However, SQL per se is far from being a database. Conversely, NoSQL and \u003Ca href=\"https://www.geeksforgeeks.org/non-relational-databases-and-their-types/\" target=\"_blank\" rel=\"noopener\">non-relational databases\u003C/a> are synonymous. NoSQL denotes \"Not Only SQL,\" signifying that these databases do not solely rely on customary SQL for data handling. NoSQL shines in scenarios where rapid management of unstructured or half-organized data at large scales is vital, but it lacks the comfortable normalized querying routines offered by SQL.\u003C/p>\r\n\u003Cp style=\"line-height: 0.5;\"> \u003C/p>\r\n\u003Ch3>\u003Ca name=\"anchor2\">\u003C/a>NoSQL sub-classes\u003C/h3>\r\n\u003Cp style=\"line-height: 0.1;\"> \u003C/p>\r\n\u003Cp>Let’s now explain a range of NoSQL subdivisions:\u003C/p>\r\n\u003Cul>\r\n\u003Cli>\u003Ca href=\"https://en.wikipedia.org/wiki/Graph_database\" target=\"_blank\" rel=\"noopener\">\u003Cstrong>Graph-based databases\u003C/strong>\u003C/a> depict data through nodes linked by edges, illustrating data entities and their interconnections. They are used in various industries, such as data intelligence, fraud elimination, artificial intelligence, ML-initiatives.\u003C/li>\r\n\u003Cli>\u003Cstrong>Key-value databases\u003C/strong> represent the most straightforward category of NoSQL databases, providing adaptable data formats. Information is organized as key-value couples, facilitating swift and robust data retrieval. They stand out in scenarios requiring high-performance, low-latency access, making them well-suited for caching and distributed tech landscapes.\u003C/li>\r\n\u003Cli>\u003Cstrong>Column-based databases\u003C/strong> emphasize columns rather than rows, each contains various data about an object, making it easy to retrieve specific info. This structure is excellent for running big data and real-time analytics, as it enables category-based searches.\u003C/li>\r\n\u003Cli>\u003Ca href=\"https://en.wikipedia.org/wiki/Document-oriented_database\" target=\"_blank\" rel=\"noopener\">\u003Cstrong>Doc-oriented\u003C/strong> \u003Cstrong>databases\u003C/strong>\u003C/a>\u003Cstrong> \u003C/strong>mean that data is stored in docs, often in JSON or BSON formats. Each doc could have a one-of-a-kind structure, and there's no need for predefined schemas. This flexibility suits content-related activities, online trade, and cooperative apps.\u003C/li>\r\n\u003C/ul>\r\n\u003Cp style=\"text-align: center;\">\u003Cimg src=\"https://blog.dexodata.com/storage/uploads/images/143/23-7-geo-targeted-proxies-selecting-databases-for-large-datasets-pic-f65b2c1c-6a56-4f89-a76b-2bf3786afc1a.png\" alt=\"How to choose a database for data scraped via proxies\" width=\"1032\" height=\"491\">\u003C/p>\r\n\u003Cp style=\"line-height: 0.5;\"> \u003C/p>\r\n\u003Ch3>\u003Ca name=\"anchor3\">\u003C/a>SQL's ACID characteristics\u003C/h3>\r\n\u003Cp style=\"line-height: 0.1;\"> \u003C/p>\r\n\u003Cp>Now, it is the moment to switch our focus towards what is setting SQL apart, namely, the ACID principles. These pillars encompass Atomicity, Consistency, Isolation, and Durability. The four principles define a transaction, ensuring data integrity. Atomicity treats each action as a single unit, preventing data loss. Consistency maintains predictable changes. Isolation prevents interference, and Durability safeguards against data loss during failures.\u003C/p>\r\n\u003Cp style=\"line-height: 0.5;\"> \u003C/p>\r\n\u003Ch3>\u003Ca name=\"anchor4\">\u003C/a>Confronting SQL against NoSQL \u003C/h3>\r\n\u003Cp style=\"line-height: 0.1;\"> \u003C/p>\r\n\u003Ctable style=\"border-collapse: collapse; width: 99.9794%; height: 450px; margin-left: auto; margin-right: auto;\" border=\"2\">\r\n\u003Ctbody>\r\n\u003Ctr style=\"height: 30px;\">\r\n\u003Ctd style=\"width: 29.1209%; height: 30px; text-align: center;\">\u003Cstrong>Facet\u003C/strong>\u003C/td>\r\n\u003Ctd style=\"text-align: center;\">\u003Cstrong>SQL fashion\u003C/strong>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 30px; text-align: center;\">\u003Cstrong>NoSQL fashion\u003C/strong>\u003C/td>\r\n\u003C/tr>\r\n\u003Ctr style=\"height: 60px;\">\r\n\u003Ctd style=\"width: 29.1209%; text-align: center; height: 60px;\">Schema\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">\u003Cspan style=\"color: #455298;\">Rigorous schema implementation \u003C/span>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">Absence of pre-established format, dynamicity\u003C/td>\r\n\u003C/tr>\r\n\u003Ctr style=\"height: 60px;\">\r\n\u003Ctd style=\"width: 29.1209%; text-align: center; height: 60px;\">Scalability questions \u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">\u003Cspan style=\"color: #455298;\">Upward extensibility, primarily constrained by hardware\u003C/span>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">Lateral extensibility, effortlessly expandable with nodes\u003C/td>\r\n\u003C/tr>\r\n\u003Ctr style=\"height: 60px;\">\r\n\u003Ctd style=\"width: 29.1209%; text-align: center; height: 60px;\">Info integrity aspects\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">\u003Cspan style=\"color: #455298;\">Guarantees data integrity and uniformity\u003C/span>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 60px;\">Lacking data consistency in comparison\u003C/td>\r\n\u003C/tr>\r\n\u003Ctr style=\"height: 90px;\">\r\n\u003Ctd style=\"width: 29.1209%; text-align: center; height: 90px;\">Transactions’ nature\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 90px;\">\u003Cspan style=\"color: #455298;\">ACID-compliant \u003C/span>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 90px;\">BASE-compliant (i.e. basically available, soft state, eventual consistency)\u003C/td>\r\n\u003C/tr>\r\n\u003Ctr style=\"height: 150px;\">\r\n\u003Ctd style=\"width: 29.1209%; text-align: center; height: 150px;\">Exemplary use cases\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 150px;\">\u003Cspan style=\"color: #455298;\">Conventional apps with intricate connections and organized data (for instance, information warehousing)\u003C/span>\u003C/td>\r\n\u003Ctd style=\"width: 33.8565%; height: 150px;\">Swift engineering, extensive-scale apps, non-organized data, and instantaneous analytics (such as big data analytics executed in real-time)\u003C/td>\r\n\u003C/tr>\r\n\u003C/tbody>\r\n\u003C/table>\r\n\u003Cp style=\"line-height: 0.5;\"> \u003C/p>\r\n\u003Ch3>\u003Ca name=\"anchor5\">\u003C/a>Databases' pluses and shortcomings summarized. SQL vs NoSQL  \u003C/h3>\r\n\u003Cp style=\"line-height: 0.1;\"> \u003C/p>\r\n\u003Cp>NoSQL pluses:\u003C/p>\r\n\u003Col>\r\n\u003Cli>Increased scalability, tailored-fit for horizontal scaling;\u003C/li>\r\n\u003Cli>Flexible with unorganized and semi-organized data; \u003C/li>\r\n\u003Cli>Elevated efficiency for simultaneous read/write processes, substantial workloads.\u003C/li>\r\n\u003C/ol>\r\n\u003Cp>NoSQL minuses:\u003C/p>\r\n\u003Cul>\r\n\u003Cli>Sophisticated queries, overall inconsistency;\u003C/li>\r\n\u003Cli>Absence of standardization, no universally applicable language for queries in place;\u003C/li>\r\n\u003Cli>Eventual consistency might potentially slow data distribution. \u003C/li>\r\n\u003C/ul>\r\n\u003Cp>SQL pluses:\u003C/p>\r\n\u003Col>\r\n\u003Cli>Strong data integrity;\u003C/li>\r\n\u003Cli>Mature ecosystem with robust tooling, accessible assistance sources;\u003C/li>\r\n\u003Cli>Normalized querying, simplifying data analysis.\u003C/li>\r\n\u003C/ol>\r\n\u003Cp>SQL minuses:\u003C/p>\r\n\u003Cul>\r\n\u003Cli>Meager vertical scalability, costly for wide-reaching operations;\u003C/li>\r\n\u003Cli>Schema rigidity makes adaptation to altering requirements problematic; \u003C/li>\r\n\u003Cli>Suboptimal hierarchical data retrieval.\u003C/li>\r\n\u003C/ul>\r\n\u003Cp>Whatever eventual directions might be, do not forget about data collection specificities. In case this aspect fails, no databases are useful. Keep on using Dexodata’s proxies with rotation. Our pool of over 1 million ethically sourced IPs from 100+ countries, including America, Canada, several EU member states, Russia, Turkey, Kazakhstan, Ukraine, etc. will suffice for any web data harvesting plans. Our 99% uptime guarantees that data gets scraped and sent to databases, seamlessly and continuously. Approach our ecosystem to buy residential and mobile proxies.\u003C/p>\r\n\u003Cp>\u003Ca href=\"https://dashboard.dexodata.com/admin/register?lang=en\" target=\"_blank\" rel=\"noopener\">Paid proxies free trial\u003C/a> is available for newcomers.\u003C/p>\u003C/body>\u003C/html>\n",[16,19,21,24,27,30,33],{"lang":17,"slug":18},"ru","vybor-bazy-dannyx-pod-krupnye-datasety",{"lang":20,"slug":6},"en",{"lang":22,"slug":23},"ua","ua-vybor-bazy-dannyx-pod-krupnye-datasety",{"lang":25,"slug":26},"cn","cn-selecting-databases-for-large-datasets",{"lang":28,"slug":29},"es","es-selecting-databases-for-large-datasets",{"lang":31,"slug":32},"fr","fr-selecting-databases-for-large-datasets",{"lang":34,"slug":35},"ar","ar-selecting-databases-for-large-datasets",[37],"Big data",[],1784902993207]