{"id":13267,"date":"2022-12-14T10:00:00","date_gmt":"2022-12-14T09:00:00","guid":{"rendered":"https:\/\/parm.com\/?p=13267"},"modified":"2026-03-02T16:08:42","modified_gmt":"2026-03-02T15:08:42","slug":"data-warehouse-and-data-lake-2","status":"publish","type":"post","link":"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/","title":{"rendered":"Data Warehouse and Data Lake"},"content":{"rendered":"\n<div class=\"et_pb_section_0 et_pb_section et_section_regular et_block_section\"><div class=\"et_pb_row_0 et_pb_row et_block_row\"><div class=\"et_pb_column_0 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_0 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h1>Data Warehouse and Data Lake<\/h1>\n<p><span style=\"font-size: large;\">Definition, similarities and differences<\/span><\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_1 et_pb_row et_block_row\"><div class=\"et_pb_column_1 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_image_0 et_pb_image et_pb_module et_block_module\"><span class=\"et_pb_image_wrap\"><img decoding=\"async\" src=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Post_Data_Warehouse_Lake.jpg\" title=\"Post_Data_Warehouse_Lake\" alt=\"Data Warehouse vs. Data Lake\" \/><\/span><\/div><\/div><\/div><div class=\"et_pb_row_2 et_pb_row et_pb_equal_columns et_block_row\"><div class=\"et_pb_column_2 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_1 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>The amount of data collected in companies is constantly increasing and with it the need to optimally manage this data and use it for analyses. Data warehouses and data lakes are established solutions for storing large amounts of data. We explain the most important differences between the two options.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_3 et_pb_row et_block_row\"><div class=\"et_pb_column_3 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_2 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_83 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#What_is_a_Data_Warehouse\" >What is a Data Warehouse?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#What_is_a_Data_Lake\" >What is a Data Lake?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#Similarities_and_differences\" >Similarities and differences<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#How_to_make_the_right_choice\" >How to make the right choice<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#The_Future_The_Data_Lakehouse\" >The Future: The Data Lakehouse<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_a_Data_Warehouse\"><\/span>What is a Data Warehouse?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The term data warehouse refers to a central collection of data, usually as part of a business intelligence solution. A large amount of data from different sources can be collected and stored here. For this purpose, the data warehouse regularly extracts data from different systems, which is then validated, cleaned, formatted and compared with already existing information. Thus, a so-called ETL process (Extract, Transform, Load) takes place. For this, the structure of the data, the so-called schema, is determined first. This procedure is called Schema on Write and determines what consolidated data looks like. Since data is usually recorded in tables, the schema answers questions such as:<\/p>\n<ul>\n<li>What does a row in a table look like?<\/li>\n<li>What attributes does each row contain?<\/li>\n<li>What data is expected?<\/li>\n<\/ul>\n<p>The resulting processed data is stored in such a way that users can access it at any time.<\/p>\n<\/div><\/div><div class=\"et_pb_image_1 et_pb_image et_pb_module et_block_module\"><span class=\"et_pb_image_wrap\"><img decoding=\"async\" src=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Data_Warehouse.jpg\" title=\"Data_Warehouse\" alt=\"Data Warehouse\" \/><\/span><\/div><div class=\"et_pb_text_3 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h3>Advantages of Data Warehouses<\/h3>\n<ul>\n<li><strong>Easy analysis: <\/strong>Since the data in a data warehouse is available in a consistent format, it can be easily analysed in a BI system and therefore used for decision-making. Even users without data technology knowledge can thus draw important insights from the available data.<strong><br \/><\/strong><\/li>\n<li><strong>Merging data: <\/strong>Since information from different databases is collected in a data warehouse, the data from different sources can be easily related to each other or analysed for correlations.<strong><br \/><\/strong><\/li>\n<li><strong>Data quality: <\/strong>Since the data is validated and formatted before it is saved, the data warehouse contains only consistent and relevant data. The quality of the available data is therefore very high.<strong><\/strong><\/li>\n<\/ul>\n<h3><\/h3>\n<h3>Disadvantages of Data Warehouses<\/h3>\n<ul>\n<li><strong>Missing data:<\/strong> In a data warehouse, only the data that was needed for the originally intended purpose is stored. If additional data is needed, it must be added to the data warehouse in a cumbersome way.<\/li>\n<li><strong>Less flexibility:<\/strong> If the purpose of the data warehouse has changed or if more data is needed in the future, the data warehouse must be adapted. This is due to the fact that the data model or structure has been defined in advance (schema on write). Changing this can be time-consuming and costly. A data warehouse is therefore less flexible for new data sources.<\/li>\n<li><strong>High start-up costs:<\/strong> Since the schema on write must be defined before starting with a data warehouse, higher costs are initially incurred.<\/li>\n<\/ul>\n<\/div><\/div><div class=\"et_pb_text_4 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"What_is_a_Data_Lake\"><\/span>What is a Data Lake?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A data lake refers to a central repository in which large amounts of data from various sources are stored, usually in raw format. But structured or semi-structured data can also be stored. So, while in a data warehouse only structured data is stored, data lakes can store information in different formats and make it available to the users in this way. In this case, the data model is only recorded in detail when the contents are read out (schema on read), which can be error-prone. An ELT process (Extract, Load, Transform) therefore takes place here.<\/p>\n<\/div><\/div><div class=\"et_pb_image_2 et_pb_image et_pb_module et_block_module\"><span class=\"et_pb_image_wrap\"><img decoding=\"async\" src=\"https:\/\/new-site.parm.com\/wp-content\/uploads\/2022\/12\/Data_Lake.jpg\" title=\"Data_Lake\" alt=\"Data Lake\" \/><\/span><\/div><div class=\"et_pb_text_5 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h3>Advantages of Data Lakes<\/h3>\n<ul>\n<li><strong>Accessibility: <\/strong>Company data is stored centrally in the data lake and can thus be easily accessed by all users.<\/li>\n<li><strong>Avoidance of data silos:<\/strong> Both structured and semi-structured or unstructured data are stored. This avoids data silos.<\/li>\n<li><strong>High flexibility:<\/strong> Data can be changed and shaped so that it can be analysed for different purposes. Additional data sources can be added easily and without major changes to the data lake.<\/li>\n<li><strong>Machine learning:<\/strong> The stored data is ideal for machine learning.<strong><\/strong><\/li>\n<\/ul>\n<h3><\/h3>\n<h3>Disadvantages of Data Lakes<\/h3>\n<ul>\n<li><strong>Storage capacity:<\/strong> Since all data is stored unfiltered, a larger memory is required than with the data warehouse.<\/li>\n<li><strong>Data quality:<\/strong> Without measures for data quality and data governance, the stored data can quickly become a so-called Data Swamp. A data swamp is an unmaintained data lake in which data is stored without appropriate documentation, so that one quickly loses the overview.<\/li>\n<\/ul>\n<\/div><\/div><div class=\"et_pb_text_6 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"Similarities_and_differences\"><\/span>Similarities and differences<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Both data warehouses and data lakes are designed for business analyses and serve as central data storage in the company. Since their purpose and goals are similar, it is easy to confuse the two technologies. The main differences are:<\/p>\n<p>&nbsp;<\/p>\n<table style=\"width: 100%; border-collapse: collapse; float: left;\" border=\"0\">\n<tbody>\n<tr>\n<td style=\"width: 33.3333%;\"><\/td>\n<td style=\"width: 33.3333%;\"><span style=\"color: #1b3e90;\"><strong>Data Warehouse<\/strong><\/span><\/td>\n<td style=\"width: 33.3333%;\"><span style=\"color: #1b3e90;\"><strong>Data Lake<\/strong><\/span><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\"><strong>Data structure<\/strong><\/td>\n<td style=\"width: 33.3333%;\">Structured, processed data<\/td>\n<td style=\"width: 33.3333%;\">Structured and semi-structured data, but mainly raw data<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\"><strong>Purpose of data storage<\/strong><\/td>\n<td style=\"width: 33.3333%;\">Defined, therefore smaller amount of data<\/td>\n<td style=\"width: 33.3333%;\">Not defined, therefore larger amount of data<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\"><strong>Users<\/strong><\/td>\n<td style=\"width: 33.3333%;\">Different users, mostly without data science knowledge<\/td>\n<td style=\"width: 33.3333%;\">Data scientists or special tools needed to translate the data for other users<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\"><strong>Changes complicated and costly<\/strong><\/td>\n<td style=\"width: 33.3333%;\">Data easily accessible, quick and easy to update<\/td>\n<td style=\"width: 33.3333%;\">Data easily accessible, quick and easy to update<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 33.3333%;\"><strong>Schema<\/strong><\/td>\n<td style=\"width: 33.3333%;\">Schema on Write: Schema is defined before data is stored<\/td>\n<td style=\"width: 33.3333%;\">Schema on Read: Schema is defined when data is read<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_4 et_pb_row et_block_row\"><div class=\"et_pb_column_4 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_7 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"How_to_make_the_right_choice\"><\/span>How to make the right choice<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Whether a data warehouse or a data lake is the better choice for you depends on various factors, which may already be evident from the differences between the two technologies. Therefore, ask yourself:<\/p>\n<ul>\n<li>Should structured data or raw data be stored?<\/li>\n<li>Is the data to serve a specific purpose?<\/li>\n<li>Who will use the data? <\/li>\n<li>How likely is it that the evaluation requirements will change?<\/li>\n<\/ul>\n<\/div><\/div><div class=\"et_pb_text_8 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"The_Future_The_Data_Lakehouse\"><\/span>The Future: The Data Lakehouse<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Often companies cannot make a clear decision whether they need a data warehouse or a data lake. Rather, they need both - a data lake to benefit from raw data, but also a data warehouse to provide analytics to all business users. However, running the two technologies at the same time has the disadvantage that the data is stored in two or more places, so it also has to be analysed, maintained and monitored in different places. This in turn can lead to errors or out-of-date data. <br \/>A data lakehouse combines the flexibility of data lakes with the structure-giving processes of a data warehouse and thus promises the best of both technologies. In this way, it is also possible to analyse unstructured data in a BI system, for example.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_5 et_pb_row et_block_row\"><div class=\"et_pb_column_5 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_9 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Data warehouses and data lakes are designed for business analyses. Both have their advantages and disadvantages, but they can also complement each other. The more suitable solution for managing your company data depends on your needs and various conditions.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_6 et_pb_row et_block_row\"><div class=\"et_pb_column_6 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_10 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>The business intelligence software myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span> is basically based on a data warehouse. This makes it possible for employees without data technology knowledge to analyse the stored data, create reports as well as diagrams and gain important insights from the data.<\/p>\n<p>However, it is also possible to connect and analyse semi-structured data in myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span>. But this requires a higher level of knowledge regarding the structure of the data model for analysing such data. This means that the expertise of a data scientist may be required.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_7 et_pb_row et_block_row\"><div class=\"et_pb_column_7 et_pb_column et_pb_column_1_2 et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_11 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>Weitere Informationen \u00fcber die Business Intelligence Software myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span>:<\/p>\n<\/div><\/div><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_0_wrapper preset--module--divi-button--default_wrapper\"><a class=\"et_pb_button_0 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-button--default\" href=\"https:\/\/parm.com\/myparm-bi\/\" target=\"_blank\">Mehr Infos<\/a><\/div><\/div><div class=\"et_pb_column_8 et_pb_column et_pb_column_1_2 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_text_12 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>M\u00f6chten Sie\u200b myPARM BI<span style=\"font-size: x-small;\"><sup>act<\/sup><\/span> in einer Demonstration kennenlernen? Dann vereinbaren Sie gleich einen Termin!<\/p>\n<\/div><\/div><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_1_wrapper preset--module--divi-button--default_wrapper\"><a class=\"et_pb_button_1 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--module--divi-button--default\" href=\"https:\/\/calendly.com\/parmag\/myparm-biact-webdemo\" target=\"_blank\">Termin vereinbaren<\/a><\/div><\/div><\/div><div class=\"et_pb_row_8 et_pb_row et_block_row\"><div class=\"et_pb_column_9 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough\"><div class=\"et_pb_code_0 et_pb_code et_pb_module\"><div class=\"et_pb_code_inner\"><div class=\"shariff shariff-align-left shariff-widget-align-left\" style=\"display:none\"><ul class=\"shariff-buttons theme-round orientation-horizontal buttonsize-medium\"><li class=\"shariff-button facebook shariff-nocustomcolor\" style=\"background-color:#4273c8\"><a href=\"https:\/\/www.facebook.com\/sharer\/sharer.php?u=https%3A%2F%2Fparm.com%2Fen%2Fdata-warehouse-and-data-lake-2%2F\" title=\"Bei Facebook teilen\" aria-label=\"Bei Facebook teilen\" role=\"button\" rel=\"nofollow\" class=\"shariff-link\" style=\"; 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Vielen Dank!\n                      <\/span>\n      <\/div>\n    <\/div>\n    <div><\/div>\n    <div id=\"sib-container\" class=\"sib-container--large sib-container--vertical\" style=\"text-align:center; background-color:rgba(27,62,144,1); max-width:1080px; border-radius:3px; border-width:0px; border-color:#C0CCD9; border-style:solid;\">\n      <form id=\"sib-form\" method=\"POST\" action=\"https:\/\/272a17fd.sibforms.com\/serve\/MUIEAOo1vFGWS7d0cvncbnGo6ZIm0MV83UA-ApqLz0GJgUd38j-GsNKlhCPZ1kVJVEGx5yChU7xjEUZEVAhHli4H01njDJsY0TOLof7HNzi7lZJvWMes1-Je1GVxivY9c8XrMzqbTrnQ9SuHnjhsRe5vXL8U5STdopGqk00vm6RWgZiOpVABZMg1kUY8-KjUWzt-Tpk5-zC9XQ2j\" data-type=\"subscription\">\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"font-size:32px; text-align:left; font-weight:700; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\">\n            <p>Newsletter<\/p>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\">\n            <div class=\"sib-text-form-block\">\n              <p>Melden Sie sich zu unserem monatlichen Newsletter an und werden Sie \u00fcber Produkte der Parm AG, Neuheiten, Trends im Projektmanagement sowie Angebote und Veranstaltungen informiert.<\/p>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-input sib-form-block\">\n            <div class=\"form__entry entry_block\">\n              <div class=\"form__label-row \">\n\n                <div class=\"entry__field\">\n                  <input class=\"input\" type=\"text\" id=\"EMAIL\" name=\"EMAIL\" autocomplete=\"off\" placeholder=\"EMAIL\" data-required=\"true\" required=\"\">\n                <\/div>\n              <\/div>\n\n              <label class=\"entry__error entry__error--primary\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n              <\/label>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-optin sib-form-block\" data-required=\"true\">\n            <div class=\"form__entry entry_mcq\">\n              <div class=\"form__label-row \">\n                <div class=\"entry__choice\">\n                  <label>\n                    <input type=\"checkbox\" class=\"input_replaced\" value=\"1\" id=\"OPT_IN\" name=\"OPT_IN\" required=\"\">\n                    <span class=\"checkbox checkbox_tick_positive\"><\/span><span style=\"font-size:14px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:transparent=\"\"><p>Ich bin damit einverstanden, dass der Newsletter individuell auf meine Interessen abgestimmt wird. Zu diesem Zweck gestatte ich der Parm AG mein \u00d6ffnungs-, Klick- und Downloadverhalten im Newsetter zu analysieren und ein personenbezogenes Nutzerprofil von mir zu erstellen. Die <a href=\"https:\/\/new-site.parm.com\/datenschutzerklaerung\" target=\"_blank\" rel=\"noopener\">Datenschutzerkl\u00e4rung <\/a>habe ich gelesen und akzeptiere diese. Die Abmeldung vom Newsletter ist jederzeit m\u00f6glich.<\/p><span data-required=\"*\" style=\"display: inline;\" class=\"entry__label entry__label_optin\"><\/span><\/span> <\/label>\n                <\/div>\n              <\/div>\n              <label class=\"entry__error entry__error--primary\" style=\"font-size:16px; text-align:left; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-color:=\"\">\n              <\/label>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"g-recaptcha\" data-sitekey=\"6LdyzNAcAAAAANv3WwEWU18I26AH-2q2CcQ6eQUk\" data-callback=\"invisibleCaptchaCallback\" data-size=\"invisible\" onclick=\"executeCaptcha\"><\/div>\n        <\/div>\n        <div style=\"padding: 8px 0;\">\n          <div class=\"sib-form-block\" style=\"text-align: left\">\n            <button class=\"sib-form-block__button sib-form-block__button-with-loader\" style=\"font-size:16px; text-align:left; font-weight:700; font-family:\" helvetica=\"\" sans-serif=\"\" color:=\"\" background-color:=\"\" border-radius:3px=\"\" border-width:0px=\"\" form=\"sib-form\" type=\"submit\">\n              <svg class=\"icon clickable__icon progress-indicator__icon sib-hide-loader-icon\" viewbox=\"0 0 512 512\">\n                <path d=\"M460.116 373.846l-20.823-12.022c-5.541-3.199-7.54-10.159-4.663-15.874 30.137-59.886 28.343-131.652-5.386-189.946-33.641-58.394-94.896-95.833-161.827-99.676C261.028 55.961 256 50.751 256 44.352V20.309c0-6.904 5.808-12.337 12.703-11.982 83.556 4.306 160.163 50.864 202.11 123.677 42.063 72.696 44.079 162.316 6.031 236.832-3.14 6.148-10.75 8.461-16.728 5.01z\"\/>\n              <\/svg>\n ANMELDEN\n            <\/button>\n          <\/div>\n        <\/div>\n\n        <input type=\"text\" name=\"email_address_check\" value=\"\" class=\"input--hidden\">\n        <input type=\"hidden\" name=\"locale\" value=\"de\">\n      <\/form>\n    <\/div>\n  <\/div>\n<\/div>\n<!-- END - We recommend to place the below code where you want the form in your website html -->\n\n<!-- START - We recommend to place the below code in footer or bottom of your website html -->\n<script>\n  window.REQUIRED_CODE_ERROR_MESSAGE = 'W\u00e4hlen Sie bitte einen L\u00e4ndervorwahl aus.';\n  window.LOCALE = 'de';\n  window.EMAIL_INVALID_MESSAGE = window.SMS_INVALID_MESSAGE = \"Die eingegebenen Informationen sind nicht g\u00fcltig. 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Bitte \u00fcberpr\u00fcfen Sie die Felder und versuchen Sie es erneut.\";\n\n\n\n\n  window.translation = {\n    common: {\n      selectedList: '{quantity} Liste ausgew\u00e4hlt',\n      selectedLists: '{quantity} Listen ausgew\u00e4hlt'\n    }\n  };\n\n  var AUTOHIDE = Boolean(0);\n<\/script>\n<script src=\"https:\/\/sibforms.com\/forms\/end-form\/build\/main.js\"><\/script>\n\n<script src=\"https:\/\/www.google.com\/recaptcha\/api.js?hl=de\"><\/script>\n\n<!-- END - We recommend to place the above code in footer or bottom of your website html -->\n<!-- End Sendinblue Form --><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The amount of data collected in companies is constantly increasing and with it the need to optimally manage this data and use it for analyses. Data warehouses and data lakes are established solutions for storing large amounts of data. We explain the most important differences between the two options.<\/p>\n","protected":false},"author":1,"featured_media":13278,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[62,63,495,494],"class_list":["post-13267","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bi-en","tag-bi","tag-business-intelligence","tag-data-lake","tag-data-warehouse"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Warehouse and Data Lake | Parm AG<\/title>\n<meta name=\"description\" content=\"Data warehouses and data lakes are established solutions for storing large amounts of data. We explain the most important differences.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/parm.com\/en\/data-warehouse-and-data-lake-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Warehouse and Data Lake\" \/>\n<meta property=\"og:description\" content=\"The amount of data collected in companies is constantly increasing and with it the need to optimally manage this data and use it for analyses. Data warehouses and data lakes are established solutions for storing large amounts of data. 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